🏗️Software Engineering: Quality Assurance (Just the Quotes)

"Program testing can be used to show the presence of bugs, but never to show their absence!" (Edsger W Dijkstra, "Notes On Structured Programming", 1970)

"It is a fundamental principle of testing that you must know in advance the answer each test case is supposed to produce. If you don't, you are not testing; you are experimenting." (Brian Kernighan, "Software Tools in Pascal", 1981)

"The essence of a software entity is a construct of interlocking concepts: […] I believe the hard part of building software to be the specification, design, and testing of this conceptual construct, not the labor of representing it and testing the fidelity of the representation." (Fred Brooks, "No Silver Bullet", 1986)

"A design remedy that prevents bugs is always preferable to a test method that discovers them." (Boris Beizer, "Software Testing Techniques", 1990)

"A test that reveals a bug has succeeded, not failed." (Boris Beizer, "Software Testing Techniques", 1990)

"More than the act of testing, the act of designing tests is one of the best bug preventers known. The thinking that must be done to create a useful test can discover and eliminate bugs before they are coded - indeed, test-design thinking can discover and eliminate bugs at every stage in the creation of software, from conception to specification, to design, coding and the rest."  (Boris Beizer, "Software Testing Techniques", 1990)

"Programmers are responsible for software quality - quality in their own work, quality in the products that incorporate their work, and quality at the interfaces between components. Quality has never been and will never be tested in. The responsibility is both moral and professional." (Boris Beizer, "Software Testing Techniques", 1990)

"Testing proves a programmer’s failure. Debugging is the programmer’s vindication." (Boris Beizer, "Software Testing Techniques", 1990)

"First law: The pesticide paradox. Every method you use to prevent or find bugs leaves a residue of subtler bugs against which those methods are ineffective."  (Boris Beizer, "Software Testing Techniques", 1990)

"Second law: The complexity barrier. Software complexity (and therefore that of bugs) grows to the limits of our ability to manage that complexity." (Boris Beizer, "Software Testing Techniques", 1990)

"Software never was perfect and won't get perfect. But is that a license to create garbage? The missing ingredient is our reluctance to quantify quality." (Boris Beizer, "Software Testing Techniques", 1990)

"Third law: Code migrates to data. Because of this law there is increasing awareness that bugs in code are only half the battle and that data problems should be given equal attention."  (Boris Beizer, "Software Testing Techniques", 1990)

"The most common kind of coding bug, and often considered the least harmful, are documentation bugs (i.e., erroneous comments). Although many documentation bugs are simple spelling errors or the result of poor writing, many are actual errors - that is, misleading or erroneous comments. We can no longer afford to discount such bugs, because their consequences are as great as 'true' coding errors. Today programming labor is dominated by maintenance. This will increase as software becomes even longer-lived. Documentation bugs lead to incorrect maintenance actions and therefore cause the insertion of other bugs." (Boris Beizer, "Software Testing Techniques", 1990)

"Testing by itself does not improve software quality. Test results are an indicator of quality, but in and of themselves, they don't improve it. Trying to improve software quality by increasing the amount of testing is like trying to lose weight by weighing yourself more often. What you eat before you step onto the scale determines how much you will weigh, and the software development techniques you use determine how many errors testing will find. If you want to lose weight, don't buy a new scale; change your diet. If you want to improve your software, don't test more; develop better." (Steve C McConnell, "Code Complete: A Practical Handbook of Software Construction", 1993)

"The source code is often the only accurate description of the software. On many projects, the only documentation available to programmers is the code itself. Requirements specifications and design documents can go out of date, but the source code is always up to date. Consequently, it's imperative that the code be of the highest possible quality." (Steve C McConnell," Code Complete: A Practical Handbook of Software Construction", 1993) 

"The real value of tests is not that they detect bugs in the code, but that they detect inadequacies in the methods, concentration, and skills of those who design and produce the code." (Charles A R Hoare, "How Did Software Get So Reliable Without Proof?", Lecture Notes in Computer Science Vol. 1051, 1996)

"A system that is comprehensively tested and passes all of its tests all of the time is a testable system. That’s an obvious statement, but an important one. Systems that aren’t testable aren’t verifiable. Arguably, a system that cannot be verified should never be deployed." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"It is a myth that we can get systems 'right the first time'. Instead, we should implement only today’s stories, then refactor and expand the system to implement new stories tomorrow. This is the essence of iterative and incremental agility. Test-driven development, refactoring, and the clean code they produce make this work at the code level." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"It is unit tests that keep our code flexible, maintainable, and reusable. The reason is simple. If you have tests, you do not fear making changes to the code! Without tests every change is a possible bug."  (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

🏗️Software Engineering: Models (Just the Quotes)

"Models are models of something, namely, [they are] reflections, representations of natural and artificial originals, that can themselves be models again. […] Models, in general, do not cover all the attributes of the originals they are representing, but only those [attributes] that seem relevant to the actual model creators and/or model users." (Herbert Stachowiak, "Allgemeine Modelltheorie", 1973)

"Models are not assigned per se uniquely to their originals. They perform their replacement function: a) for definite – cognitive and/or handling, model-using – subjects, b) within definite time intervals, c) under restrictions of definite operations of thought or fact. […] Models are not only models of something. They are also models for somebody, a human or an artificial model user. They perform thereby their functions in time, within a time interval. And finally, they are models for a definite purpose." (Herbert Stachowiak, "Allgemeine Modelltheorie", 1973)

"A model is an attempt to represent some segment of reality and explain, in a simplified manner, the way the segment operates." (E Frank Harrison, "The managerial decision-making process" , 1975)

"The aim of the model is of course not to reproduce reality in all its complexity. It is rather to capture in a vivid, often formal, way what is essential to understanding some aspect of its structure or behavior." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"A pattern is a fully realized form original, or model accepted or proposed for imitation. With patterns, small piecework is standardized into a larger chunk or unit. Patterns become the building blocks for design and construction. Finding and applying patterns indicates progress in a field of human endeavor." (Peter Coad, "Object-oriented patterns", 1992)

"Model building is the art of selecting those aspects of a process that are relevant to the question being asked. As with any art, this selection is guided by taste, elegance, and metaphor; it is a matter of induction, rather than deduction. High science depends on this art." (John H Holland," Hidden Order: How Adaptation Builds Complexity", 1995)

"Another curious phenomenon you may meet is in fitting data to a model there are errors in both the data and the model. For example, a normal distribution may be assumed, but the tails may in fact be larger or smaller than the model predicts, and possibly no negative values can occur although the normal distribution allows them. Thus there are two sources of error. As your ability to make more accurate measurements increases the error due to the model becomes an increasing part of the error." (Richard Hamming, "The Art of Doing Science and Engineering: Learning to Learn", 1997)

"We tend to form mental models that are simpler than reality; so if we create represented models that are simpler than the actual implementation model, we help the user achieve a better understanding. […] Understanding how software actually works always helps someone to use it, but this understanding usually comes at a significant cost. One of the most significant ways in which computers can assist human beings is by putting a simple face on complex processes and situations. As a result, user interfaces that are consistent with users’ mental models are vastly superior to those that are merely reflections of the implementation model." (Alan Cooper et al,  "About Face 3: The Essentials of Interaction Design", 2007)

"The methods of science include controlled experiments, classification, pattern recognition, analysis, and deduction. In the humanities we apply analogy, metaphor, criticism, and (e)valuation. In design we devise alternatives, form patterns, synthesize, use conjecture, and model solutions." (Béla H Bánáthy, "Designing Social Systems in a Changing World", 1996)

"If the design, or some central part of it, does not map to the domain model, that model is of little value, and the correctness of the software is suspect. At the same time, complex mappings between models and design functions are difficult to understand and, in practice, impossible to maintain as the design changes. A deadly divide opens between analysis and design so that insight gained in each of those activities does not feed into the other." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Models come in many varieties and serve many roles, even those restricted to the context of a software development project. Domain-driven design calls for a model that doesn’t just aid early analysis but is the very foundation of the design […]  Tightly relating the code to an underlying model gives the code meaning and makes the model relevant." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"The effectiveness of an overall design is very sensitive to the quality and consistency of fine-grained design and implementation decisions. With a MODEL-DRIVEN DESIGN, a portion of the code is an expression of the model; changing that code changes the model. Programmers are modelers, whether anyone likes it or not. So it is better to set up the project so that the programmers do good modeling work." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"The technical model that drives the software development process must be strictly pared down to the necessary minimum to fulfill its functions. An explanatory model can include aspects of the domain that provide context that clarifies the more narrowly scoped model. Explanatory models offer the freedom to create much more communicative styles tailored to a particular topic. Visual metaphors used by the domain experts in a field often present clearer explanations, educating developers and harmonizing experts. Explanatory models also present the domain in a way that is simply different, and multiple, diverse explanations help people learn." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"To create software that is valuably involved in users' activities, a development team must bring to bear a body of knowledge related to those activities. The breadth of knowledge required can be daunting. The volume and complexity of information can be overwhelming. Models are tools for grappling with this overload. A model is a selectively simplified and consciously structured form of knowledge. An appropriate model makes sense of information and focuses it on a problem." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"When a design is based on a model that reflects the basic concerns of the users and domain experts, the bones of the design can BE revealed to the user to a greater extent than with other design approaches. Revealing the model gives the user more access to the potential of the software and yields consistent, predictable behavior." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"The use of rapid prototyping methodologies is to reduce the production time by using working models of the final product early in a project tends to eliminate time-consuming revisions later on, and by completing design tasks concurrently, rather than sequentially throughout the project. The steps are crunched together to reduce the amount of time needed to develop training or a product. The design and development phases are done simultaneously and the formative evaluation is done throughout the process." (Irene Chen, "Instructional Design Methodologies", 2008)

"A good system design is based on a sound conceptual model (architecture). A system design that has no conceptual structure and little logic to its organization is ultimately going to be unsuccessful. Good architecture will address all the requirements of the system at the right level of abstraction." (Vasudeva Varma, "Software Architecture: A Case Based Approach", 2009)

"Although it might seem as though frittering away valuable time on sketches and models and simulations will slow work down, prototyping generates results faster." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Less apparent is that qualitatively different problem-solving techniques are required at high levels of complexity than at low ones. Purely analytical techniques, powerful for the lower levels, can be overwhelmed at the higher ones. At higher levels, architecting methods, experience-based heuristics, abstraction, and integrated modeling must be called into play."  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"Modeling is the creation of abstractions or representations of the system to predict and analyze performance, costs, schedules, and risks and to provide guidelines for systems research, development, design, manufacture, and management. Modeling is the centerpiece of systems architecting - a mechanism of communication to clients and builders, of design management with engineers and designers, of maintaining system integrity with project management, and of learning for the architect, personally."  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"Modeling is the fabric of architecting because architecting is at a considerable distance of abstraction from actual construction. The architect does not manipulate the actual elements of construction. The architect builds models that are passed into more detailed design processes. Those processes lead, eventually, to construction drawings or the equivalent and actual system fabrication or coding."  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"The basic idea behind all of these techniques is to simplify problem solving by concentrating on its essentials. Consolidate and simplify the objectives. Focus on the things with the highest impact, things that determine other things. Put to one side minor issues likely to be resolved by the resolution of major ones. Discard the nonessentials. Model (abstract) the system at as high a level as possible, then progressively reduce the level of abstraction. In short: Simplify!"  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"A complete data analysis will involve the following steps: (i) Finding a good model to fit the signal based on the data. (ii) Finding a good model to fit the noise, based on the residuals from the model. (iii) Adjusting variances, test statistics, confidence intervals, and predictions, based on the model for the noise." (DeWayne R Derryberry, "Basic data analysis for time series with R", 2014)

 "A wide variety of statistical procedures (regression, t-tests, ANOVA) require three assumptions: (i) Normal observations or errors. (ii) Independent observations (or independent errors, which is equivalent, in normal linear models to independent observations). (iii) Equal variance - when that is appropriate (for the one-sample t-test, for example, there is nothing being compared, so equal variances do not apply)." (DeWayne R Derryberry, "Basic data analysis for time series with R", 2014)

"Once a model has been fitted to the data, the deviations from the model are the residuals. If the model is appropriate, then the residuals mimic the true errors. Examination of the residuals often provides clues about departures from the modeling assumptions. Lack of fit - if there is curvature in the residuals, plotted versus the fitted values, this suggests there may be whole regions where the model overestimates the data and other whole regions where the model underestimates the data. This would suggest that the current model is too simple relative to some better model." (DeWayne R Derryberry, "Basic data analysis for time series with R", 2014)

"Prediction about the future assumes that the statistical model will continue to fit future data. There are several reasons this is often implausible, but it also seems clear that the model will often degenerate slowly in quality, so that the model will fit data only a few periods in the future almost as well as the data used to fit the model. To some degree, the reliability of extrapolation into the future involves subject-matter expertise." (DeWayne R Derryberry, "Basic data analysis for time series with R", 2014)

 "The random element in most data analysis is assumed to be white noise - normal errors independent of each other. In a time series, the errors are often linked so that independence cannot be assumed (the last examples). Modeling the nature of this dependence is the key to time series." (DeWayne R Derryberry, "Basic data analysis for time series with R", 2014)

"Design is the process of taking something that appears in the mind’s eye, modeling it in one or more of a number of ways, predicting how that thing will behave if it is made, and then making it, sometimes modifying the design as we make it. Design is what engineering is about. Furthermore, modeling is how engineering design is done. This includes mental models, mathematical models, computer models, plans and drawings, written language, and (sometimes) physical models." (William M Bulleit, "The Engineering Way of Thinking: The Idea", Structure [magazine], 2015) 

🏗️Software Engineering: Annotations (Just the Quotes)

"Don’t comment bad code - rewrite it." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Don't just echo the code with comments make every comment count." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Make sure comments and code agree." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Notice that even the data is commented. One of the most effective ways to document a program is simply to describe the data layout in detail. If you can specify for each important variable what values it can assume and how it gets changed, you have gone a long way to describing the program. [...] Document your data layouts." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"The best documentation for a computer program is a clean structure. It also helps if the code is well formatted, with good mnemonic identifiers and labels (if any are needed), and a smattering of enlightening comments. Flowcharts and program descriptions are of secondary importance; the only reliable documentation of a computer program is the code itself. The reason is simple -whenever there are multiple representations of a program, the chance for discrepancy exists. If the code is in error, artistic flowcharts and detailed comments are to no avail. Only by reading the code can the programmer know for sure what the program does." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Writing a computer program eventually boils down to writing a sequence of statements in the language at hand. How each of those statements is expressed determines in large measure the intelligibility of the whole; no amount of commenting, formatting, or supplementary documentation can entirely replace well expressed statements. After all, they determine what the program actually does." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"The most common kind of coding bug, and often considered the least harmful, are documentation bugs (i.e., erroneous comments). Although many documentation bugs are simple spelling errors or the result of poor writing, many are actual errors - that is, misleading or erroneous comments. We can no longer afford to discount such bugs, because their consequences are as great as 'true' coding errors. Today programming labor is dominated by maintenance. This will increase as software becomes even longer-lived. Documentation bugs lead to incorrect maintenance actions and therefore cause the insertion of other bugs." (Boris Beizer, "Software Testing Techniques", 1990)

"Any comment that forces you to look in another module for the meaning of that comment has failed to communicate to you and is not worth the bits it consumes." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Obsolete comments are worse than no comments." (Douglas Crockford, "JavaScript: The Good Parts", 2008)

Business Applications: Enterprise Resource Planning (Definitions)

"An industry term for the broad set of activities supported by multi-module application software that helps a manufacturer or other business manage the important parts of its business, including product planning, parts purchasing, maintaining inventories, interacting with suppliers, providing customer service, and tracking orders." (Timothy J  Kloppenborg et al, "Project Leadership", 2003)

"A system that ties together and automates the diverse components of a company’s supply-chain operations, including the order, fulfillment, staffing, and accounting processes." (Evan Levy & Jill Dyché, "Customer Data Integration", 2006)

"An information system that coordinates resources that are needed to perform business functions such as order fulfillment and billing." (Tony Fisher, "The Data Asset", 2009)

"Complex computer systems that manage financial and operational data and processes. ERP systems generally include a general ledger, subsystems such as accounts payable and accounts receivable, inventory and manufacturing, supply chain, and logistics applications. Also, an enterprise-wide computer system that manages and coordinates a business's resources, information, and functions from shared data stores." (Janice M Roehl-Anderson, "IT Best Practices for Financial Managers", 2010)

"Integrated computer-based application software used to manage internal and external resources." (Martin Oberhofer et al, "The Art of Enterprise Information Architecture", 2010)

"An integrated software application that serves as a business management tool, helping to track resources, tangible assets, materials, finances, suppliers, vendors, customers, and employees." (Gina Abudi & Brandon Toropov, "The Complete Idiot's Guide to Best Practices for Small Business", 2011)

"Software that integrates the planning, management, and use of all resources in the entire enterprise; also called enterprise systems." (Linda Volonino & Efraim Turban, "Information Technology for Management 8th Ed", 2011)

"System that supports databases and automates business processes such as production, distribution, human resources, and financial accounting; often includes both financial and nonfinancial information. (46, 280)" (Leslie G Eldenburg & Susan K Wolcott, "Cost Management" 2nd Ed, 2011)

"Set of applications and systems that a company uses to manage its resources across the entire enterprise." (Bill Holtsnider & Brian D Jaffe, "IT Manager's Handbook" 3rd Ed, 2012)

"A packaged set of business applications that combines business rules, processes, and data management into a single integrated environment to support a business." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A variety of software-based systems that aim to standardize processes and information management within organizations, typically focusing on operational processes including finance and accounting, supply chain and logistics, inventory management, and resource management." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Expansion of MRP activities to include the coordination of other supply chain activities such as shipping, ordering, warehousing, and quality assurance." (Kenneth A Shaw, "Integrated Management of Processes and Information", 2013)

"An integrated application that controls day-to-day business operations such as inventory, sales, finance, human resources, and distribution. From the warehousing perspective, ERP systems differ from standard databases in that they have predefined data models that must be understood in order to successfully extract the data." (Jim Davis & Aiman Zeid, "Business Transformation: A Roadmap for Maximizing Organizational Insights", 2014)

🕸Systems Engineering: Architecture (Just the Quotes)

"Thus, the central theme that runs through my remarks is that complexity frequently takes the form of hierarchy, and that hierarchic systems have some common properties that are independent of their specific content. Hierarchy, I shall argue, is one of the central structural schemes that the architect of complexity uses." (Herbert A Simon, "The Architecture of Complexity", Proceedings of the American Philosophical Society Vol. 106 (6), 1962)

"From a functional point of view, mental models can be described as symbolic structures which permit people: to generate descriptions of the purpose of a system, to generate descriptions of the architecture of a system, to provide explanations of the state of a system, to provide explanations of the functioning of a system, to make predictions of future states of a system." (Gert Rickheit & Lorenz Sichelschmidt, "Mental Models: Some Answers, Some Questions, Some Suggestions", 1999)

"Most systems displaying a high degree of tolerance against failures are a common feature: Their functionality is guaranteed by a highly interconnected complex network. A cell's robustness is hidden in its intricate regulatory and metabolic network; society's resilience is rooted in the interwoven social web; the economy's stability is maintained by a delicate network of financial and regulator organizations; an ecosystem's survivability is encoded in a carefully crafted web of species interactions. It seems that nature strives to achieve robustness through interconnectivity. Such universal choice of a network architecture is perhaps more than mere coincidences." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"The word ‘symmetry’ conjures to mind objects which are well balanced, with perfect proportions. Such objects capture a sense of beauty and form. The human mind is constantly drawn to anything that embodies some aspect of symmetry. Our brain seems programmed to notice and search for order and structure. Artwork, architecture and music from ancient times to the present day play on the idea of things which mirror each other in interesting ways. Symmetry is about connections between different parts of the same object. It sets up a natural internal dialogue in the shape." (Marcus du Sautoy, "Symmetry: A Journey into the Patterns of Nature", 2008)

"A graph enables us to visualize a relation over a set, which makes the characteristics of relations such as transitivity and symmetry easier to understand. […] Notions such as paths and cycles are key to understanding the more complex and powerful concepts of graph theory. There are many degrees of connectedness that apply to a graph; understanding these types of connectedness enables the engineer to understand the basic properties that can be defined for the graph representing some aspect of his or her system. The concepts of adjacency and reachability are the first steps to understanding the ability of an allocated architecture of a system to execute properly." (Dennis M Buede, "The Engineering Design of Systems: Models and methods", 2009)

"The simplest basic architecture of an artificial neural network is composed of three layers of neurons - input, output, and intermediary (historically called perceptron). When the input layer is stimulated, each node responds in a particular way by sending information to the intermediary level nodes, which in turn distribute it to the output layer nodes and thereby generate a response. The key to artificial neural networks is in the ways that the nodes are connected and how each node reacts to the stimuli coming from the nodes it is connected to. Just as with the architecture of the brain, the nodes allow information to pass only if a specific stimulus threshold is passed. This threshold is governed by a mathematical equation that can take different forms. The response depends on the sum of the stimuli coming from the input node connections and is 'all or nothing'." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"A key discovery of network science is that the architecture of networks emerging in various domains of science, nature, and technology are similar to each other, a consequence of being governed by the same organizing principles. Consequently we can use a common set of mathematical tools to explore these systems."  (Albert-László Barabási, "Network Science", 2016)

🏗️Software Engineering: Innovation (Just the Quotes)

"Engineering is a profession, an art of action and synthesis and not simply a body of knowledge. Its highest calling is to invent and innovate." (Daniel V DeSimone & Hardy Cross, "Education for Innovation", 1968)

"Technological invention and innovation are the business of engineering. They are embodied in engineering change." (Daniel V DeSimone & Hardy Cross, "Education for Innovation", 1968)

"More software projects have gone awry for lack of calendar time than for all other causes combined. Why is this cause of disaster so common?
First, our techniques of estimating are poorly developed. More seriously, they reflect an unvoiced assumption which is quite untrue, i.e., that all will go well. Second, our estimating techniques fallaciously confuse effort with progress, hiding the assumption that men and months are interchangeable. Third, because we are uncertain of our estimates, software managers often lack the courteous stubbornness of Antoine's chef. Fourth, schedule progress is poorly monitored. Techniques proven and routine in other engineering disciplines are considered radical innovations in software engineering. Fifth, when schedule slippage is recognized, the natural (and traditional) response is to add manpower." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"Issues of quality, timeliness and change are the conditions that are forcing us to face up to the issues of enterprise architecture. The precedent of all the older disciplines known today establishes the concept of architecture as central to the ability to produce quality and timely results and to manage change in complex products. Architecture is the cornerstone for containing enterprise frustration and leveraging technology innovations to fulfill the expectations of a viable and dynamic Information Age enterprise." (John Zachman, "Enterprise Architecture: The Issue of The Century", 1997)

"If you want a culture of innovation, you can’t punish people for attempting great things and sometimes failing." (Bill Joy, "Large Problem: How big companies can innovate", Fortune, 2004)

"Although it can at times seem forbiddingly abstract, design thinking is embodied thinking - embodied in teams and projects, to be sure, but embodied in the physical spaces of innovation as well." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Breaking rules is indeed an important part of creativity. Innovation needs a level of guidance." (Pearl Zhu,  "Digitizing Boardroom: The Multifaceted Aspects of Digital Ready Boards", 2016)

"The art of questioning is to ignite innovative thinking; the science of questioning is to frame system thinking, with the progressive pursuit of better solutions." (Pearl Zhu, "Leadership Master: Five Digital Trends to Leap Leadership Maturity", 2016)

"Organizations that rely too heavily on org charts and matrixes to split and control work often fail to create the necessary conditions to embrace innovation while still delivering at a fast pace. In order to succeed at that, organizations need stable teams and effective team patterns and interactions. They need to invest in empowered, skilled teams as the foundation for agility and adaptability. To stay alive in ever more competitive markets, organizations need teams and people who are able to sense when context changes and evolve accordingly." (Matthew Skelton & Manuel Pais, "Team Topologies: Organizing Business and Technology Teams for Fast Flow", 2019)

"Progress comes from finding better ways to do things. Don’t be afraid of innovation. Don’t be afraid of ideas that are not your own." (Douglas Crockford, [response to David Winer])

🏗️Software Engineering: Control (Just the Quotes)

"The term architecture is used here to describe the attributes of a system as seen by the programmer, i.e., the conceptual structure and functional behavior, as distinct from the organization of the data flow and controls, the logical design, and the physical implementation." (Gene Amdahl et al, "Architecture of the IBM System", IBM Journal of Research and Development. Vol 8 (2), 1964)

"A computer program is shaped by its data representation and the statements that determine its flow of control. These define the structure of a program. There is no sharp distinction between expression and organization; it is more a question of scope." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Choosing a better data structure is often an art, which we cannot teach. Often you must write a preliminary draft of the code before you can determine what changes in the data structure will help simplify control. [...] Choose a data representation that makes the program simple." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Jumping around unnecessarily in a computer program has proved to be a fruitful source of errors, and usually indicates that the programmer is not entirely in control of the code." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"The purpose of a programming system is to make a computer easy to use. To do this, it furnishes languages and various facilities that are in fact programs invoked and controlled by language features. But these facilities are bought at a price: the external description of a programming system is ten to twenty times as large as the external description of the computer system itself. The user finds it far easier to specify any particular function, but there are far more to choose from, and far more options and formats to remember." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"Controlling complexity is the essence of computer programming." (Brian W Kernighan, "Software Tools", 1976)

"With increasing size and complexity of the implementations of information systems, it is necessary to use some logical construct (or architecture) for defining and controlling the interfaces and the integration of all of the components of the system." (John Zachman, "A Framework for Information Systems Architecture", 1987)

"The underlying message of all these rules is that inheritance tends to work against the primary technical imperative you have as a programmer, which is to manage complexity. For the sake of controlling complexity, you should maintain a heavy bias against inheritance." (Steve C McConnell," Code Complete: A Practical Handbook of Software Construction", 1993)

"Good software designs accommodate change without huge investments and rework. When we use code that is out of our control, special care must be taken to protect our investment and make sure future change is not too costly." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Successful reproduction [of bugs] is all about control. If you control all the relevant variables, you will reproduce your problem. The trick, of course, is identifying which variables are relevant to the bug at hand, discovering what you need to set them to, and finding a way to do so." (Paul Butcher, "Debug It! Find, Repair, and Prevent Bugs in Your Code", 2009)

"Acceptance testing relies on the ability to execute automated tests in a productionlike environment. However, a vital property of such a test environment is that it is able to successfully support automated testing. Automated acceptance testing is not the same as user acceptance testing. One of the differences is that automated acceptance tests should not run in an environment that includes integration to all external systems. Instead, your acceptance testing should be focused on providing a controllable environment in which the system under test can be run. 'Controllable' in this context means that you are able to create the correct initial state for our tests. Integrating with real external systems removes our ability to do this." (David Farley & Jez Humble, "Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation", 2010)

"I do not dispute that there are large variations in reported measurements of programmer productivity; however, close examination of the evidence suggests that this observed variability originates in vague definitions of the term, in unreliable instruments of measurement, or in uncontrolled environmental factors, much more than it does in the intrinsic capabilities of programmers at comparable levels of training and experience." (Laurent Bossavit, "The Leprechauns of Software Engineering", 2015)

🏗️Software Engineering: Assumptions (Just the Quotes)

"At the heart of reengineering is the notion of discontinuous thinking - of recognizing and breaking away from the outdated rules and fundamental assumptions that underlie operations. Unless we change these rules, we are merely rearranging the deck chairs on the Titanic. We cannot achieve breakthroughs in performance by cutting fat or automating existing processes. Rather, we must challenge old assumptions and shed the old rules that made the business underperform in the first place." (Michael M Hammer, "Reengineering Work: Don't Automate, Obliterate", Magazine, 1990)

"Design bugs are often subtle and occur by evolution with early assumptions being forgotten as new features or uses are added to systems." (Fernando J Corbató, "On Building Systems That Will Fail", 1991)

"All things which are proved to be impossible must obviously rest on some assumptions, and when one or more of these assumptions are not true then the impossibility proof fails - but the expert seldom remembers to carefully inspect the assumptions before making their 'impossible' statements." (Richard Hamming, "The Art of Doing Science and Engineering: Learning to Learn", 1997)

"We build models to increase productivity, under the justified assumption that it's cheaper to manipulate the model than the real thing. Models then enable cheaper exploration and reasoning about some universe of discourse. One important application of models is to understand a real, abstract, or hypothetical problem domain that a computer system will reflect. This is done by abstraction, classification, and generalization of subject-matter entities into an appropriate set of classes and their behavior." (Stephen J Mellor, "Executable UML: A Foundation for Model-Driven Architecture", 2002)

"When we set out to write software, we never know enough. Knowledge on the project is fragmented, scattered among many people and documents, and it’s mixed with other information so that we don’t even know which bits of knowledge we really need. Domains that seem less technically daunting can be deceiving: we don’t realize how much we don’t know. This ignorance leads us to make false assumptions" (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Because it so often takes ages before a new piece of software is ready for anyone to use, programmers often test their assumptions against 'use cases' hypothetical scenarios about how imaginary people might need or want to use a program." (Scott Rosenberg, "Dreaming in Code", 2007)

"Every piece of code is built upon a platform of myriad assumptions - things that have to be true for it to behave as expected. More often than not, bugs arise because one or more of these assumptions are violated or turn out to be mistaken." (Paul Butcher, "Debug It! Find, Repair, and Prevent Bugs in Your Code", 2009)

"Everything you do is based upon a foundation of assumptions. You can’t possibly avoid making them, and it’s crazy to try - you can’t work from first principles every time. But assumptions are dangerous, because they create blind spots - things you treat as true without necessarily having direct evidence." (Paul Butcher, "Debug It! Find, Repair, and Prevent Bugs in Your Code", 2009)

"The fundamental assumption underlying all software projects is that software is easy to change. If you violate this assumption by creating inflexible structures, then you undercut the economic model that the entire industry is based on." (Robert C Martin, "The Clean Coder: A code of conduct for professional programmers", 2011)

"An architecture that scales well for a particular application is built around assumptions of which operations will be common and which will be rare - the load parameters. If those assumptions turn out to be wrong, the engineering effort for scaling is at best wasted, and at worst counterproductive." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"It would be unwise to assume that faults are rare and simply hope for the best. It is important to consider a wide range of possible faults - even fairly unlikely ones - and to artificially create such situations in your testing environment to see what happens. In distributed systems, suspicion, pessimism, and paranoia pay off." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

🏗️Software Engineering: Software Deployment (Just the Quotes)

"A system that is comprehensively tested and passes all of its tests all of the time is a testable system. That’s an obvious statement, but an important one. Systems that aren’t testable aren’t verifiable. Arguably, a system that cannot be verified should never be deployed." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Releasing software should be easy. It should be easy because you have tested every single part of the release process hundreds of times before. It should be as simple as pressing a button. The repeatability and reliability derive from two principles: automate almost everything, and keep everything you need to build, deploy, test, and release your application in version control." (David Farley & Jez Humble, "Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation", 2010)

"So, when should you think about automating a process? The simplest answer is, 'When you have to do it a second time.' The third time you do something, it should be done using an automated process. This fine-grained incremental approach rapidly creates a system for automating the repeated parts of your development, build, test, and deployment process." (David Farley & Jez Humble, "Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation", 2010)

"The deployment pipeline has its foundations in the process of continuous integration and is in essence the principle of continuous integration taken to its logical conclusion. The aim of the deployment pipeline is threefold. First, it makes every part of the process of building, deploying, testing, and releasing software visible to everybody involved, aiding collaboration. Second, it improves feedback so that problems are identified, and so resolved, as early in the process as possible. Finally, it enables teams to deploy and release any version of their software to any environment at will through a fully automated process." (David Farley & Jez Humble, "Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation", 2010)

"In essence, Continuous Integration is about reducing risk by providing faster feedback. First and foremost, it is designed to help identify and fix integration and regression issues faster, resulting in smoother, quicker delivery, and fewer bugs. By providing better visibility for both technical and non-technical team members on the state of the project, Continuous Integration can open and facilitate communication channels between team members and encourage collaborative problem solving and process improvement. And, by automating the deployment process, Continuous Integration helps you get your software into the hands of the testers and the end users faster, more reliably, and with less effort." (John F Smart, "Jenkins: The Definitive Guide", 2011)

"System engineering is concerned with all aspects of the development and evolution of complex systems where software plays a major role. System engineering is therefore concerned with hardware development, policy and process design and system deployment, as well as software engineering. System engineers are involved in specifying the system, defining its overall architecture, and then integrating the different parts to create the finished system. They are less concerned with the engineering of the system components (hardware, software, etc.)." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"A value stream is a series of activities required to deliver an outcome. The software development value stream may be described as: validate business case, analyze, design, build, test, deploy, learn from usage analytics and other feedback - rinse and repeat." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"Continuous deployment is but one of many powerful tools at your disposal for increasing iteration speed. Other options include investing in time-saving tools, improving your debugging loops, mastering your programming workflows, and, more generally, removing any bottlenecks that you identify." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Why is continuous deployment such a powerful tool? Fundamentally, it allows engineers to make and deploy small, incremental changes rather than the larger, batched changes typical at other companies. That shift in approach eliminates a significant amount of overhead associated with traditional release processes, making it easier to reason about changes and enabling engineers to iterate much more quickly." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

🏗️Software Engineering: Interfaces (Just the Quotes)

"A good information system both exposes interface errors and stimulates their correction" (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"By the architecture of a system, I mean the complete and detailed specification of the user interface. For a computer this is the programming manual. For a compiler it is the language manual. For a control program it is the manuals for the language or languages used to invoke its functions. For the entire system it is the union of the manuals the user must consult to do his entire job." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"Clearly, methods of designing programs so as to eliminate or at least illuminate side effects can have an immense payoff in maintenance costs. So can methods of implementing designs with fewer people, fewer interfaces, and hence fewer bugs." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"With increasing size and complexity of the implementations of information systems, it is necessary to use some logical construct (or architecture) for defining and controlling the interfaces and the integration of all of the components of the system." (John Zachman, "A Framework for Information Systems Architecture", 1987)

"Programmers are responsible for software quality - quality in their own work, quality in the products that incorporate their work, and quality at the interfaces between components. Quality has never been and will never be tested in. The responsibility is both moral and professional." (Boris Beizer, "Software Testing Techniques", 1990)

"A problem with this 'waterfall' approach is that there will then be no user interface to test with real users until this last possible moment, since the 'intermediate work products' do not explicitly separate out the user interface in a prototype with which users can interact. Experience also shows that it is not possible to involve the users in the design process by showing them abstract specifications documents, since they will not understand them nearly as well as concrete prototypes." (Jakob Nielsen, "Usability Engineering", 1993)

"One should not start full-scale implementation efforts based on early user interface designs. Instead, early usability evaluation can be based on prototypes of the final systems that can be developed much faster and much more cheaply, and which can thus be changed many times until a better understanding of the user interface design has been achieved." (Jakob Nielsen, "Usability Engineering", 1993)

"Scenarios are an especially cheap kind of prototype. […] Scenarios are the ultimate reduction of both the level of functionality and of the number of features: They can only simulate the user interface as long as a test user follows a previously planned path. […] Scenarios are the ultimate minimalist prototype in that they describe a single interaction session without any flexibility for the user. As such, they combine the limitations of both horizontal prototypes (users cannot interact with real data) and vertical prototypes (users cannot move freely through the system)." (Jakob Nielsen, "Usability Engineering", 1993)

"The difference between standards and guidelines is that a standard specifies how the interface should appear to the user, whereas a set of guidelines provides advice about the usability characteristics of the interface." (Jakob Nielsen, "Usability Engineering", 1993)

"Users often do not know what is good for them. […] Users have a very hard time predicting how they will interact with potential future systems with which they have no experience. […] Furthermore, users will often have divergent opinions when asked about details of user interface design." (Jakob Nielsen, "Usability Engineering", 1993)

"As the least conscious layer of the user experience, the conceptual model has the paradoxical quality of also having the most impact on usability. If an appropriate conceptual model is faithfully represented throughout the interface, after users recognize and internalize the model, they will have a fundamental understanding of what the application does and how to operate it." (Bob Baxley, "Making the Web Work: Designing Effective Web Applications", 2002)

"Modularity, an approach that separates a large system into simpler parts that are individually designed and operated, incorrectly assumes that complex system behavior can essentially be reduced to the sum of its parts. A planned decomposition of a system into modules works well for systems that are not too complex. […] However, as systems become more complex, this approach forces engineers to devote increasing attention to designing the interfaces between parts, eventually causing the process to break down."  (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"On small, informal projects, a lot of design is done while the programmer sits at the keyboard. 'Design' might be just writing a class interface in pseudocode before writing the details. It might be drawing diagrams of a few class relationships before coding them. It might be asking another programmer which design pattern seems like a better choice. Regardless of how it’s done, small projects benefit from careful design just as larger projects do, and recognizing design as an explicit activity maximizes the benefit you will receive from it." (Steve C McConnell, "Code Complete: A Practical Handbook of Software Construction" 2nd Ed., 2004)

"Enterprise architecture is the organizing logic for business processes and IT infrastructure reflecting the integration and standardization requirements of a company's operation model. […] The key to effective enterprise architecture is to identify the processes, data, technology, and customer interfaces that take the operating model from vision to reality." (Jeanne W Ross et al, "Enterprise architecture as strategy: creating a foundation for business", 2006)

"An abstraction is not a module, or an interface, class, or method; it is a structure, pure and simple - an idea reduced to its essential form. Since the same idea can be reduced to different forms, abstractions are always, in a sense, inventions, even if the ideas they reduce existed before in the world outside the software. The best abstractions, however, capture their underlying ideas so naturally and convincingly that they seem more like discoveries." (Daniel Jackson, "Software Abstractions", 2006)

"Software is built on abstractions. Pick the right ones, and programming will flow naturally from design; modules will have small and simple interfaces; and new functionality will more likely fit in without extensive reorganization […] Pick the wrong ones, and programming will be a series of nasty surprises: interfaces will become baroque and clumsy as they are forced to accommodate unanticipated interactions, and even the simplest of changes will be hard to make." (Daniel Jackson, "Software Abstractions", 2006)

"We tend to form mental models that are simpler than reality; so if we create represented models that are simpler than the actual implementation model, we help the user achieve a better understanding. […] Understanding how software actually works always helps someone to use it, but this understanding usually comes at a significant cost. One of the most significant ways in which computers can assist human beings is by putting a simple face on complex processes and situations. As a result, user interfaces that are consistent with users’ mental models are vastly superior to those that are merely reflections of the implementation model." (Alan Cooper et al,  "About Face 3: The Essentials of Interaction Design", 2007)

"Structural patterns describe how classes and objects can be combined to form larger structures. Patterns for classes describe how inheritance can be used to provide more useful program interfaces. Patterns for objects describe how objects can be composed into larger structures using object composition." (Junji Nakano et al, "Programming Statistical Data Visualization in the Java Language" [in "Handbook of Data Visualization"], 2008)

"Good architecture provides good interfaces that separate the shear layers of its implementation: a necessity for evolution and maintenance. Class-oriented programming puts both data evolution and method evolution in the same shear layer: the class. Data tend to remain fairly stable over time, while methods change regularly to support new services and system operations. The tension in these rates of change stresses the design." (James O Coplien & Trygve Reenskaug, "The DCI Paradigm: Taking Object Orientation into the Architecture World", 2014)

"When an engineer refactors the internals of a system without modifying its interface, whether for performance, clarity, or any other reason, the system’s tests shouldn’t need to change. The role of tests in this case is to ensure that the refactoring didn’t change the system’s behavior. Tests that need to be changed during a refactoring indicate that either the change is affecting the system’s behavior and isn’t a pure refactoring, or that the tests were not written at an appropriate level of abstraction." (Titus Winters, "Software Engineering at Google: Lessons Learned from Programming Over Time", 2020)

🏗️Software Engineering: Risks (Just the Quotes)

"The major distinguishing feature of the spiral model is that it creates a risk-driven approach to the software process rather than a primarily document-driven or code-driven process. It incorporates many of the strengths of other models and resolves many of their difficulties." (Barry Boehm, "A spiral model of software development and enhancement", IEEE, 1988)

"Refactoring is risky. It requires changes to working code that can introduce subtle bugs. Refactoring, if not done properly, can set you back days, even weeks. And refactoring becomes riskier when practiced informally or ad hoc." (Erich Gamma, 2002)

"The business of believing only what you have a right to believe is called risk management." (Tom DeMarco & Timothy Lister, "Waltzing with Bears: Managing Risk on Software Projects", 2003)

"Developing fewer features allows you to conserve development resources and spend more time refining those features that users really need. Fewer features mean fewer things to confuse users, less risk of user errors, less description and documentation, and therefore simpler Help content. Removing any one feature automatically increases the usability of the remaining ones." (Jakob Nielsen, "Prioritizing Web Usability", 2006)

"Duplication is the primary enemy of a well-designed system. It represents additional work, additional risk, and additional unnecessary complexity."  (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Modeling is the creation of abstractions or representations of the system to predict and analyze performance, costs, schedules, and risks and to provide guidelines for systems research, development, design, manufacture, and management. Modeling is the centerpiece of systems architecting - a mechanism of communication to clients and builders, of design management with engineers and designers, of maintaining system integrity with project management, and of learning for the architect, personally."  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"In essence, Continuous Integration is about reducing risk by providing faster feedback. First and foremost, it is designed to help identify and fix integration and regression issues faster, resulting in smoother, quicker delivery, and fewer bugs. By providing better visibility for both technical and non-technical team members on the state of the project, Continuous Integration can open and facilitate communication channels between team members and encourage collaborative problem solving and process improvement. And, by automating the deployment process, Continuous Integration helps you get your software into the hands of the testers and the end users faster, more reliably, and with less effort." (John F Smart, "Jenkins: The Definitive Guide", 2011)

"Systems with high risks must be tested more thoroughly than systems that do not generate big losses if they fail. The risk assessment must be done for the individual system parts, or even for single error possibilities. If there is a high risk for failures by a system or subsystem, there must be a greater testing effort than for less critical (sub)systems. International standards for production of safety-critical systems use this approach to require that different test techniques be applied for software of different integrity levels." (Andreas Spillner et al, "Software Testing Foundations: A Study Guide for the Certified Tester Exam" 4th Ed., 2014)

"Sometimes you can’t fit everything in. Remember that the sprint is great for testing risky solutions that might have a huge payoff. So you’ll have to reverse the way you would normally prioritize. If a small fix is so good and low-risk that you’re already planning to build it next week, then seeing it in a prototype won’t teach you much. Skip those easy wins in favor of big, bold bets." (Jake Knapp et al, "Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days", 2016)

🏗️Software Engineering: Abstraction (Just the Quotes)

"The effective exploitation of his powers of abstraction must be regarded as one of the most vital activities of a competent programmer." (Edsger W Dijkstra, "The Humble Programmer", 1972)

"Object-oriented programming increases the value of these metrics by managing this complexity. The most effective tool available for dealing with complexity is abstraction. Many types of abstraction can be used, but encapsulation is the main form of abstraction by which complexity is managed in object-oriented programming. Programming in an object-oriented language, however, does not ensure that the complexity of an application will be well encapsulated. Applying good programming techniques can improve encapsulation, but the full benefit of object-oriented programming can be realized only if encapsulation is a recognized goal of the design process." (Rebecca Wirfs-Brock," Object-Oriented Design: A responsibility-driven approach", 1989)

"All of engineering involves some creativity to cover the parts not known, and almost all of science includes some practical engineering to translate the abstractions into practice." (Richard W Hamming, "The Art of Probability for Scientists and Engineers", 1991)

"In object-oriented analysis, we seek to model the world by identifying the classes and objects that form the vocabulary of the problem domain, and in object-oriented design, we invent the abstractions and mechanisms that provide the behavior that this model requires." (Grady Booch, "Object-Oriented Design: With Applications", 1991)

"Watch for coupling that's too tight. 'Coupling' refers to how tight the connection is between two classes. In general, the looser the connection, the better. Several general guidelines flow from this concept: Minimize accessibility of classes and members. Avoid friend classes, because they're tightly coupled. Make data private rather than protected in a base class to make derived classes less tightly coupled to the base class. Avoid exposing member data in a class's public interface. Be wary of semantic violations of encapsulation. Observe the 'Law of Demeter' [...]. Coupling goes hand in glove with abstraction and encapsulation. Tight coupling occurs when an abstraction is leaky, or when encapsulation is broken." (Steve C McConnell," Code Complete: A Practical Handbook of Software Construction", 1993)

"Generically, an architecture is the description of the set of components and the relationships between them. […] A software architecture describes the layout of the software modules and the connections and relationships among them. A hardware architecture can describe how the hardware components are organized. However, both these definitions can apply to a single computer, a single information system, or a family of information systems. Thus 'architecture' can have a range of meanings, goals, and abstraction levels, depending on who’s speaking." (Frank J Armour et al, "A big-picture look at enterprise architectures", IT professional Vol 1 (1), 1999)

"Abstraction is the ability to engage with a concept while safely ignoring some of its details - handling different details at different levels. Any time you work with an aggregate, you're working with an abstraction. […] From a complexity point of view, the principal benefit of abstraction is that it allows you to ignore irrelevant details. Most real-world objects are already abstractions of some kind." (Steve C McConnell, "Code Complete: A Practical Handbook of Software Construction" 2nd Ed., 2004)

"Encapsulation picks up where abstraction leaves off. Abstraction says, ‘You're allowed to look at an object at a high level of detail’. Encapsulation says, ‘Furthermore, you aren't allowed to look at an object at any other level of detail’." (Steve C McConnell, "Code Complete: A Practical Handbook of Software Construction" 2nd Ed., 2004)

"A commitment to simplicity of design means addressing the essence of design - the abstractions on which software is built - explicitly and up front. Abstractions are articulated, explained, reviewed and examined deeply, in isolation from the details of the implementation. This doesn’t imply a waterfall process, in which all design and specification precedes all coding. But developers who have experienced the benefits of this separation of concerns are reluctant to rush to code, because they know that an hour spent on designing abstractions can save days of refactoring." (Daniel Jackson, "Software Abstractions", 2006)

"Abstractions matter to users too. Novice users want programs whose abstractions are simple and easy to understand; experts want abstractions that are robust and general enough to be combined in new ways. When good abstractions are missing from the design, or erode as the system evolves, the resulting program grows barnacles of complexity. The user is then forced to master a mass of spurious details, to develop workarounds, and to accept frequent, inexplicable failures." (Daniel Jackson, "Software Abstractions", 2006)

"An abstraction is not a module, or an interface, class, or method; it is a structure, pure and simple - an idea reduced to its essential form. Since the same idea can be reduced to different forms, abstractions are always, in a sense, inventions, even if the ideas they reduce existed before in the world outside the software. The best abstractions, however, capture their underlying ideas so naturally and convincingly that they seem more like discoveries." (Daniel Jackson, "Software Abstractions", 2006)

"Software is built on abstractions. Pick the right ones, and programming will flow naturally from design; modules will have small and simple interfaces; and new functionality will more likely fit in without extensive reorganization […] Pick the wrong ones, and programming will be a series of nasty surprises: interfaces will become baroque and clumsy as they are forced to accommodate unanticipated interactions, and even the simplest of changes will be hard to make." (Daniel Jackson, "Software Abstractions", 2006)

"Communicating abstractions unambiguously - from programmer to machine, from programmer to programmer, and from program to user - is the single most challenging demand of software development." (Scott Rosenberg, "Dreaming in Code", 2007)

"A good system design is based on a sound conceptual model (architecture). A system design that has no conceptual structure and little logic to its organization is ultimately going to be unsuccessful. Good architecture will address all the requirements of the system at the right level of abstraction." (Vasudeva Varma, "Software Architecture: A Case Based Approach", 2009)

"Less apparent is that qualitatively different problem-solving techniques are required at high levels of complexity than at low ones. Purely analytical techniques, powerful for the lower levels, can be overwhelmed at the higher ones. At higher levels, architecting methods, experience-based heuristics, abstraction, and integrated modeling must be called into play."  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"Modeling is the creation of abstractions or representations of the system to predict and analyze performance, costs, schedules, and risks and to provide guidelines for systems research, development, design, manufacture, and management. Modeling is the centerpiece of systems architecting - a mechanism of communication to clients and builders, of design management with engineers and designers, of maintaining system integrity with project management, and of learning for the architect, personally."  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"Modeling is the fabric of architecting because architecting is at a considerable distance of abstraction from actual construction. The architect does not manipulate the actual elements of construction. The architect builds models that are passed into more detailed design processes. Those processes lead, eventually, to construction drawings or the equivalent and actual system fabrication or coding."  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"The basic idea behind all of these techniques is to simplify problem solving by concentrating on its essentials. Consolidate and simplify the objectives. Focus on the things with the highest impact, things that determine other things. Put to one side minor issues likely to be resolved by the resolution of major ones. Discard the nonessentials. Model (abstract) the system at as high a level as possible, then progressively reduce the level of abstraction. In short: Simplify!"  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"A model is an abstraction of the system being studied rather than an alternative representation of that system. Ideally, a representation of a system should maintain all the information about the entity being represented. An abstraction deliberately simplifies and picks out the most salient characteristics." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"Design patterns are high-level abstractions that document successful design solutions. They are fundamental to design reuse in object-oriented development." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"Adopting a mindset of instrumentation means ensuring we have a set of dashboards that surface key health metrics and that enable us to drill down to the relevant data. However, many of the questions we want to answer tend to be exploratory, since we often don’t know everything that we want to measure ahead of time. Therefore, we need to build flexible tools and abstractions that make it easy to track additional metrics." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"But like many other aspects of code quality, building an abstraction for a problem comes with tradeoffs. Building a generalized solution takes more time than building one specific to a given problem. To break even, the time saved by the abstraction for future engineers needs to outweigh the time invested." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"The principle of abstraction advocates the simplification of entities through reduction and generalization: reduction is by elimination of unnecessary details and generalization is by identification and specification of common and important characteristics." (Girish Suryanarayana et al, "Refactoring for Software Design Smells: Managing Technical Debt", 2015)

"When an engineer refactors the internals of a system without modifying its interface, whether for performance, clarity, or any other reason, the system’s tests shouldn’t need to change. The role of tests in this case is to ensure that the refactoring didn’t change the system’s behavior. Tests that need to be changed during a refactoring indicate that either the change is affecting the system’s behavior and isn’t a pure refactoring, or that the tests were not written at an appropriate level of abstraction." (Titus Winters, "Software Engineering at Google: Lessons Learned from Programming Over Time", 2020)

🏗️Software Engineering: Classes (Just the Quotes)

"A subsystem is a set of classes (and possibly other subsystems) collaborating to fulfill a set of responsibilities. Although subsystems do not exist as the software executes, they are useful conceptual entities." (Rebecca Wirfs-Brock, "Object-oriented Design: A. responsibility-driven approach", 1989)

"The data-driven approach to object-oriented design focuses on the structure of the data in a system. This results in the incorporation of structural information in the definitions of classes. Doing so violates encapsulation. The responsibility-driven approach emphasizes the encapsulation of both the structure and behavior of objects. By focusing on the contractual responsibilities of a class, the designer is able to postpone implementation considerations until the implementation phase. While responsibility-driven design is not the only technique addressing this problem, most other techniques attempt to enforce encapsulation during the implementation phase. This is too late in the software life-cycle to achieve maximum benefits." (Rebecca Wirfs-Brock, "Object-oriented design: a responsibility-driven approach", 1989)

"In object-oriented analysis, we seek to model the world by identifying the classes and objects that form the vocabulary of the problem domain, and in object-oriented design, we invent the abstractions and mechanisms that provide the behavior that this model requires." (Grady Booch, "Object-Oriented Design: With Applications", 1991) 

"Object-oriented analysis is a method of analysis that examines requirements from the perspective of the classes and objects found in the vocabulary of the problem domain."(Grady Booch, "Object-oriented design: With Applications", 1991)

"Object-oriented programming is a method of implementation in which programs are organized as cooperative collections of objects, each of which represents an instance of some class, and whose classes are all members of a hierarchy of classes united via inheritance relationships." (Grady Booch, "Object-oriented design: With Applications", 1991)

"Whereas object-oriented analysis typically focuses upon one specific problem at a time, domain analysis seeks to identify the classes and objects that are common to all applications within a given domain, such as missile avionics systems, compilers, or accounting software." (Grady Booch, "Object-oriented design: With Applications", 1991)

"Object-oriented methods tend to focus on the lowest-level building block: the class and its objects." (Peter Coad, "Object-oriented patterns", 1992)

"If you're passing a parameter among several routines, that might indicate a need to factor those routines into a class that share the parameter as object data. Streamlining parameter passing isn't a goal, per se, but passing lots of data around suggests that a different class organization might work better." (Steve C McConnell," Code Complete: A Practical Handbook of Software Construction", 1993)

"[...] inheritance is a powerful tool for reducing complexity because a programmer can focus on the generic attributes of an object without worrying about the details. If a programmer must be constantly thinking about semantic differences in subclass implementations, then inheritance is increasing complexity rather than reducing it." (Steve C McConnell," Code Complete: A Practical Handbook of Software Construction", 1993)

"Inheritance is the idea that one class is a specialization of another class. The purpose of inheritance is to create simpler code by defining a base class that specifies common elements of two or more derived classes. The common elements can be routine interfaces, implementations, data members, or data types. Inheritance helps avoid the need to repeat code and data in multiple locations by centralizing it within a base class. When you decide to use inheritance, you have to make several decisions: For each member routine, will the routine be visible to derived classes? Will it have a default implementation? Will the default implementation be overridable? For each data member (including variables, named constants, enumerations, and so on), will the data member be visible to derived classes?" (Steve C McConnell," Code Complete: A Practical Handbook of Software Construction", 1993)

"Modularity's goal is to make each routine or class like a 'black box': You know what goes in, and you know what comes out, but you don't know what happens inside." (Steve C McConnell," Code Complete: A Practical Handbook of Software Construction", 1993)

"Watch for coupling that's too tight. 'Coupling' refers to how tight the connection is between two classes. In general, the looser the connection, the better. Several general guidelines flow from this concept: Minimize accessibility of classes and members. Avoid friend classes, because they're tightly coupled. Make data private rather than protected in a base class to make derived classes less tightly coupled to the base class. Avoid exposing member data in a class's public interface. Be wary of semantic violations of encapsulation. Observe the 'Law of Demeter' [...]. Coupling goes hand in glove with abstraction and encapsulation. Tight coupling occurs when an abstraction is leaky, or when encapsulation is broken." (Steve C McConnell," Code Complete: A Practical Handbook of Software Construction", 1993)

"Often you'll see the same three or four data items together in lots of places: fields in a couple of classes, parameters in many method signatures. Bunches of data that hang around together really ought to be made into their own object." (Kent Beck, "Refactoring: Improving the Design of Existing Code", 1999)

"An object-oriented application is a set of interacting objects. Each object is an implementation of one or more roles. A role supports a set of related (cohesive) responsibilities. A responsibility is an obligation to perform a task or know certain information. And objects don't work in isolation, they collaborate with others in a community to perform the overall responsibilities of the application. So a conceptual view, at least to start, is a distillation of the key object roles and their responsibilities (stated at a fairly high level). More than likely (unless you form classification hierarchies and use inheritance and composition techniques) many candidates you initially model will map directly to a single class in some inheritance hierarchy. But I like to open up possibilities by think first of roles and responsibilities, and then as a second step towards a specification-level view, mapping these candidates to classes and interfaces." (Rebecca Wirfs-Brock, [interview] 2003)

"On small, informal projects, a lot of design is done while the programmer sits at the keyboard. 'Design' might be just writing a class interface in pseudocode before writing the details. It might be drawing diagrams of a few class relationships before coding them. It might be asking another programmer which design pattern seems like a better choice. Regardless of how it’s done, small projects benefit from careful design just as larger projects do, and recognizing design as an explicit activity maximizes the benefit you will receive from it." (Steve C McConnell, "Code Complete: A Practical Handbook of Software Construction" 2nd Ed., 2004)

"An abstraction is not a module, or an interface, class, or method; it is a structure, pure and simple - an idea reduced to its essential form. Since the same idea can be reduced to different forms, abstractions are always, in a sense, inventions, even if the ideas they reduce existed before in the world outside the software. The best abstractions, however, capture their underlying ideas so naturally and convincingly that they seem more like discoveries." (Daniel Jackson, "Software Abstractions", 2006)

"Clean code is focused. Each function, each class, each module exposes a single-minded attitude that remains entirely undistracted, and unpolluted, by the surrounding details."  (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Every system is built from a domain-specific language designed by the programmers to describe that system. Functions are the verbs of that language, and classes are the nouns."  (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

🏗️Software Engineering: Modularity (Just the Quotes)

"Make sure every module hides something." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Make the coupling between modules visible." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Most programs are too big to be comprehended as a single chunk. They must be divided into smaller pieces that can be conquered separately. That is the only way to write them reliably; it is the only way to read and understand them. [...] When a program is not broken up into small enough pieces, the larger modules often fail to deliver on these promises. They try to do too much, or too many different things, and hence are difficult to maintain and are too specialized for general use." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"When the operation to be done is more complex, write a separate subroutine or function. The ease of later comprehending, debugging, and changing the program will more than compensate for any overhead caused by adding the extra modules." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Write and test a big program in small pieces." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"A good top-down design avoids bugs in several ways. First, the clarity of structure and representation makes the precise statement of requirements and functions of the modules easier. Second, the partitioning and independence of modules avoids system bugs. Third, the suppression of detail makes flaws in the structure more apparent. Fourth, the design can be tested at each of its refinement steps, so testing can start earlier and focus on the proper level of detail at each step." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"The programmer's primary weapon in the never-ending battle against slow system is to change the intramodular structure. Our first response should be to reorganize the modules' data structures." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"By pulling together all of the decisions affecting the choice of modules and interrelationships in a system, we necessarily affect the way in which other decisions are organized and resolved. Thus, some issues which have traditionally been approached in a certain way during the earliest phase of a project may have to be dealt with in an entirely different manner at a much later stage once the designer graduates to a structured design approach." (Edward Yourdon & Larry L Constantine, "Structured Design: Fundamentals of a discipline of computer program and systems design", 1978)

"Elements (lines of code) in a coincidentally-cohesive module have no relationship. Typically occurs as the result of modularizing existing code, to separate out redundant code." (Edward Yourdon & Larry L Constantine, "Structured Design: Fundamentals of a discipline of computer program and systems design", 1978)

"Wherever there is modularity there is the potential for misunderstanding: Hiding information implies a need to check communication." (Alan J Perlis, "Epigrams on Programming", 1982)

"Most of us managers are prone to one failing: A tendency to manage people as though they were modular components." (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"Extra features were once considered desirable. We now recognize that 'free' features are rarely free. Any increase in generality that does not contribute to reliability, modularity, maintainability, and robustness should be suspected." (Boris Beizer, "Software Testing Techniques", 1990)

"Modularity's goal is to make each routine or class like a 'black box': You know what goes in, and you know what comes out, but you don't know what happens inside." (Steve C McConnell," Code Complete: A Practical Handbook of Software Construction", 1993)

"The concept of modularity is related to information hiding, encapsulation, and other design heuristics. But sometimes thinking about how to assemble a system from a set of black boxes provides insights that information hiding and encapsulation don't, so the concept is worth having in your back pocket." (Steve C McConnell," Code Complete: A Practical Handbook of Software Construction", 1993)

"Modularity, an approach that separates a large system into simpler parts that are individually designed and operated, incorrectly assumes that complex system behavior can essentially be reduced to the sum of its parts. A planned decomposition of a system into modules works well for systems that are not too complex. […] However, as systems become more complex, this approach forces engineers to devote increasing attention to designing the interfaces between parts, eventually causing the process to break down."  (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"Whether you are designing systems or individual modules, never forget to use the simplest thing that can possibly work." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Design is the bridging activity between gathering and implementation of software requirements that satisfies the required needs. […] The fundamental goal of design is to reduce the number of dependencies between modules, thus reducing the complexity of the system. This is also known as coupling; lesser the coupling the better is the design. On the other hand, higher the binding between elements within a module (known as cohesion) the better is the design." (Vasudeva Varma, "Software Architecture: A Case Based Approach", 2009)

"The principle of modularization advocates the creation of cohesive and loosely coupled abstractions through techniques such as localization and decomposition." (Girish Suryanarayana et al, "Refactoring for Software Design Smells: Managing Technical Debt", 2015)

🏗️Software Engineering: Domains (Just the Quotes)

"[Object-oriented analysis is] the challenge of understanding the problem domain and then the system's responsibilities in that light." (Edward Yourdon, "Object-Oriented Design", 1991) 

"To us, analysis is the study of a problem domain, leading to a specification of externally observable behavior; a complete, consistent, and feasible statement of what is needed; a coverage of both functional and quantified operational characteristics (e. g. reliability, availability, performance)." (Edward Yourdon, Object-oriented design, 1991)

"As the size of software systems increases, the algorithms and data structures of the computation no longer constitute the major design problems. When systems are constructed from many components, the organization of the overall system - the software architecture - presents a new set of design problems. This level of design has been addressed in a number of ways including informal diagrams and descriptive terms, module interconnection languages, templates and frameworks for systems that serve the needs of specific domains, and formal models of component integration mechanisms." (David Garlan & Mary Shaw, "An introduction to software architecture", Advances in software engineering and knowledge engineering Vol 1, 1993)

"Design patterns are not about designs such as linked lists and hash tables that can be encoded in classes and reused as is. Nor are they complex, domain-specific designs for an entire application or subsystem. The design patterns [...] are descriptions of communicating objects and classes that are customized to solve a general design problem in a particular context." (Erich Gamma et al, "Design Patterns: Elements of Reusable Object-Oriented Software", 1994)

"Domain-driven design is both a way of thinking and a set of priorities, aimed at accelerating software projects that have to deal with complicated domains." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"If the architecture isolates the domain-related code in a way that allows a cohesive domain design loosely coupled to the rest of the system, then that architecture can probably support domain-driven DESIGN." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"If the design, or some central part of it, does not map to the domain model, that model is of little value, and the correctness of the software is suspect. At the same time, complex mappings between models and design functions are difficult to understand and, in practice, impossible to maintain as the design changes. A deadly divide opens between analysis and design so that insight gained in each of those activities does not feed into the other." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"The technical model that drives the software development process must be strictly pared down to the necessary minimum to fulfill its functions. An explanatory model can include aspects of the domain that provide context that clarifies the more narrowly scoped model. Explanatory models offer the freedom to create much more communicative styles tailored to a particular topic. Visual metaphors used by the domain experts in a field often present clearer explanations, educating developers and harmonizing experts. Explanatory models also present the domain in a way that is simply different, and multiple, diverse explanations help people learn." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Every system is built from a domain-specific language designed by the programmers to describe that system. Functions are the verbs of that language, and classes are the nouns."  (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Enterprise architecture [is] a coherent whole of principles, methods, and models that are used in the design and realisation of an enterprise's organisational structure, business processes, information systems, and infrastructure. […] The most important characteristic of an enterprise architecture is that it provides a holistic view of the enterprise. […] To achieve this quality in enterprise architecture, bringing together information from formerly unrelated domains necessitates an approach that is understood by all those involved from those different domains." (Marc Lankhorst, "Enterprise Architecture at Work: Modelling, Communication and Analysis", 2009)

"Trying to determine the cognitive load of software using simple measures such as lines of code, number of modules, classes, or methods is misguided. […] When measuring cognitive load, what we really care about is the domain complexity - how complex is the problem that we’re trying to solve with software? A domain is a more largely applicable concept than software size." (Matthew Skelton, "Team Topologies: Organizing Business and Technology Teams for Fast Flow", 2019)

"Knowledge graphs use an organizing principle so that a user (or a computer system) can reason about the underlying data. The organizing principle gives us an additional layer of organizing data (metadata) that adds connected context to support reasoning and knowledge discovery. […] Importantly, some processing can be done without knowledge of the domain, just by leveraging the features of the property graph model (the organizing principle)." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)