#️⃣Software Engineering: Mea Culpa (Part VI: A Look Back)

Software Engineering Series

Looking back at my university years, I'd say that there are three teachers, respectively courses that made a considerable impact on students' lives. In the second year I learned Category Algebra, which despite the fact that it reflected past knowledge and the topics were too complex for most of us, it provided us with a unprecedented layer of abstraction that showed us that Mathematics is not what we thought it to be!

The second course was related to the Complex plane theory, a course in which, the decan of the university at those times, challenged our way of thinking about relatively basic concepts. It was a big gap between what we thought about Mathematics, and what the subject proved to be. The course was thought in a post-university year together with a course on Relativity Theory, in which even we haven't understood much about the concepts and theories, it was the first time (except the Graph theory), we saw applied Mathematics to a broader context. Please don't misunderstand me! There were many other valuable teachers and courses, though these were the three courses that made the most important impact for me!

During those times, we attended also courses on Fortran, Pascal, C++, HTML and even dBase, and, even if each programming language brought something new in the landscape, I can't say they changed how we thought about the world (some of us had similar courses during the lyceum years) and problem solving. That's what for example SQL or more generally a database-related course brought, even if I had to wait for the first MooC courses to appear. Equally important was also Scott E Page's course on Model Theory, which introduced the model-thinking approach, a structured way of thinking about models, with applicability to the theoretical and practical aspects of life.

These are the courses that anybody interested in programming and/or IT should attend! Of course, there are also courses on algorithms, optimization, linear and non-linear programming, and they bring an arsenal of concepts and techniques to think about, though, even if they might have a wide impact, I can't compare them with the courses mentioned above. A course should (ideally) change the way we think about the world to make a sensible difference! Same goes for programming and theoretical concepts too!...

Long after I graduated, I found many books and authors that I wished I had met earlier! Quotable Math reflects some of the writings I found useful, though now it seems already too late for those books to make a considerable impact! Conversely, it's never too late to find new ways to look at life, and this is what some books achieve! This is also a way of evaluating critically what we want to read or what is worth reading!

Of course, there are many courses, books or ideas out there, though if they haven't changed the way you think about life, directly or indirectly, are they worth attending, respectively reading? Conversely, if one hasn't found a new perspective brought by a topic, probably one barely scratched the surface of the subject, independently if we talk here about students or teachers. For some topics, it's probably too much to ask, though pragmatically talking, that's the intrinsic value of what we learn! 

That's a way to think about life and select the books worth reading! I know, many love reading for the sake of reading, though the value of a book, theory, story or other similar artifacts should be judged especially by the impact they have on our way of thinking, respectively on our lives. Just a few ideas that's maybe worth reflective upon... 

#️⃣Software Engineering: Mea Culpa (Part II: The Beginnings)

Software Engineering Series

I started programming at 14-15 years old with logical schemas made on paper, based mainly on simple mathematical algorithms like solving equations of second degree, finding prime or special numbers, and other simple tricks from the mathematical world available for a student at that age. It was challenging to learn programming based only on schemas, though, looking back, I think it was the best learning basis a programmer could have, because it allowed me thinking logically and it was also a good exercise, as one was forced to validate mentally or on paper the outputs.

Then I moved to learning Basic and later Pascal on old generation Spectrum computers, mainly having a keyboard with 64K memory and an improvised monitor. It felt almost like a holiday when one had the chance to work 45 minutes or so on an IBM computer with just 640K memory. It was also a motivation to stay long after hours to write a few more lines of code. Even if it made no big difference in what concerns the speed, the simple idea of using a more advanced computer was a big deal.

The jump from logical schemas to actual programming was huge, as we moved from static formulas to exploratory methods like the ones of finding the roots of equations of upper degrees by using approximation methods, working with permutations and a few other combinatoric tools, interpolation methods, and so on. Once I got my own 64K Spectrum keyboard, a new world opened, having more time to play with 2- and 3-dimensional figures, location problems and so on. It was probably the time I got most interesting exposure to things not found in the curricula.  

Further on, during the university years I moved to Fortran, back to Pascal and dBASE, and later to C and C++, the focus being further on mathematical and sorting algorithms, working with matrices, and so on. I have to admit that it was a big difference between the students who came from 2-3 hours of Informatics per week (like I did) and the ones coming from lyceums specialized on Informatics, this especially during years in which learning materials were almost inexistent. In the end all went well.

The jumping through so many programming languages, some quite old for the respective times, even if allowed acquiring different perspectives, it felt sometimes like  a waste of time, especially when one was limited to using the campus computers, and that only during lab hours. That was the reality of those times. Fortunately, the university years went faster than they came. Almost one year after graduation, with a little help, some effort and benevolence, I managed to land a job as web developer, jumping from an interlude with Java to ASP, JavaScript, HTML, ColdFusion, ActionScript, SQL, XML and a few other programming languages ‘en vogue’ during the 2000.

Somewhere between graduation and my first job, my life changed when I was able to buy my own PC (a Pentium). It was the best investment I could make, mainly because it allowed me to be independent of what I was doing at work. It allowed me learning the basics of OOP programming based on Visual Basic and occasionally on Visual C++ and C#. Most of the meaningful learning happened after work, from the few books available, full of mistakes and other challenges.

That was my beginning. It is not my intent to brag about how much or how many programming languages I learned - knowledge is anyway relative - but to differentiate between the realities of then and today, as a bridge over time.

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#️⃣Software Engineering: Programming (Part XIV: Good Programmer, Bad Programmer)

Software Engineering Series

The use of denominations like 'good' or 'bad' related to programmers and programming carries with it a thin separation between these two perceptional poles that represent the end results of the programming process, reflecting the quality of the code delivered, respectively the quality of a programmer’s effort and  behavior as a whole. This means that the usage of the two denominations is often contextual, 'good' and 'bad' being moving points on a imaginary value scale with a wide range of values within and outside the interval determined by the two.

The 'good programmer' label is a idealization of the traits associated with being a programmer – analyzing and understanding the requirements, filling the gaps when necessary, translating the requirements in robust designs, developing quality code with a minimum of overwork, delivering on-time, being able to help others, to work as part of a (self-organizing) team and alone, when the project requires it, to follow methodologies, processes or best practices, etc. The problem with such a definition is that there's no fix limit, considering that programmer’s job description can include an extensive range of requirements.

The 'bad programmer' label is used in general when programmers (repeatedly) fail to reach others’ expectations, occasionally the labeling being done independently of one’s experience in the field. The volume of bugs and mistakes, the fuzziness of designs and of the code written, the lack of comments and documentation, the lack of adherence to methodologies, processes, best practices and naming conventions are often considered as indicators for such labels. Sometimes even the smallest mistakes or the wrong perceptions of one’s effort and abilities can trigger such labels.

Labeling people as 'good' or 'bad' has the tendency of reinforcing one's initial perception, in extremis leading to self-fulfilling prophecies - predictions that directly or indirectly cause themselves to become true, by the very terms on how the predictions came into being. Thus, when somebody labels another as 'good' or 'bad' he more likely will look for signs that reinforce his previous believes. This leads to situations in which "good" programmers’ mistakes are easier overlooked than 'bad' programmers' mistakes, even if the mistakes are similar.

A good label can in theory motivate, while a bad label can easily demotivate, though their effects depend from person to person. Such labels can easily become a problem for beginners, because they can easily affect beginners' perception about themselves. It’s so easy to forget that programming is a continuous learning process in which knowledge is relative and highly contextual, each person having strengths and weaknesses.

Each programmer has a particular set of skills that differentiate him from other programmers. Each programmer is unique, aspect reflected in the code one writes. Expecting programmers to fit an ideal pattern is unrealistic. Instead of using labels one should attempt to strengthen the weaknesses and make adequate use of a person’s strengths. In this approach resides the seeds for personal growth and excellence.

There are also programmers who excel in certain areas - conceptual creativity, ability in problem identification, analysis and solving, speed, ingenuity of design and of making best use of the available tools, etc. Such programmers, as Randall Stross formulates it, “are an order of magnitude better” than others. The experience and skills harnessed with intelligence have this transformational power that is achievable by each programmer in time.

Even if we can’t always avoid such labeling, it’s important to become aware of the latent force the labels carry with them, the effect they have on our colleagues and teammates. A label can easily act as a boomerang, hitting us back long after it was thrown away.

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#️⃣Software Engineering: Programming (Part XIII: Misconceptions about Programming II)

Continuation

One of the organizational stereotypes is having a big room full of cubicles filled with employees. Even if programmers can work in such settings, improperly designed environments restrict to a certain degree the creativity and productivity, making more difficult employees' collaboration and socialization. Despite having dedicated meeting rooms, an important part of the communication occurs ad-hoc. In open spaces each transient interruption can easily lead inadvertently to loss of concentration, which leads to wasted time, as one needs retaking thoughts’ thread and reviewing the last written code, and occasionally to bugs.

Programming is expected to be a 9 to 5 job with the effective working time of 8 hours. Subtracting the interruptions, the pauses one needs to take, the effective working time decreases to about 6 hours. In other words, to reach 8 hours of effective productivity one needs to work about 10 hours or so. Therefore, unless adequately planned, each project starts with a 20% of overtime. Moreover, even if a task is planned to take 8 hours, given the need of information the allocated time is split over multiple days. The higher the need for further clarifications the higher the chances for effort to expand. In extremis, the effort can double itself.

Spending extensive time in front of the computer can have adverse effects on programmers’ physical and psychical health. Same effect has the time pressure and some of the negative behavior that occurs in working environments. Also, the communication skills can suffer when they are not properly addressed. Unfortunately, few organizations give importance to these aspects, few offer a work free time balance, even if a programmer’s job best fits and requires such approach. What’s even more unfortunate is when organizations ignore the overtime, taking it as part of job’s description. It’s also one of the main reasons why programmers leave, why competent workforce is lost. In the end everyone’s replaceable, however what’s the price one must pay for it?

Trainings are offered typically within running projects as they can be easily billed. Besides the fact that this behavior takes time unnecessarily from a project’s schedule, it can easily make trainings ineffective when the programmers can’t immediately use the new knowledge. Moreover, considering resources that come and go, the unwillingness to invest in programmers can have incalculable effects on an organization performance, respectively on their personal development.

Organizations typically look for self-motivated resources, this request often encompassing organization’s whole motivational strategy. Long projects feel like a marathon in which is difficult to sustain the same rhythm for the whole duration of the project. Managers and team leaders need to work on programmers’ motivation if they need sustained performance. They must act as mentors and leaders altogether, not only to control tasks’ status and rave and storm each time deviations occur. It’s easy to complain about the status quo without doing anything to address the existing issues (challenges).

Especially in dysfunctional teams, programmers believe that management can’t contribute much to project’s technical aspects, while management sees little or no benefit in making developers integrant part of project's decisional process. Moreover, the lack of transparence and communication lead to a wide range of frictions between the various parties.

Probably the most difficult to understand is people’s stubbornness in expecting different behavior by following the same methods and of ignoring the common sense. It’s bewildering the easiness with which people ignore technological and Project Management principles and best practices. It resides in human nature the stubbornness of learning on the hard way despite the warnings of the experienced, however, despite the negative effects there’s often minimal learning in the process...

To be eventually continued…

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#️⃣Software Engineering: Programming (Part XII: Misconceptions about Programming - Part I)

Software Engineering Series

Besides equating the programming process with a programmer’s capabilities, minimizing the importance of programming and programmers’ skills in the whole process (see previous post), there are several other misconceptions about programming that influence process' outcomes.

Having a deep knowledge of a programming language allows programmers to easily approach other programming languages, however each language has its own learning curve ranging from a few weeks to half of year or more. The learning curve is dependent on the complexity of the languages known and the language to be learned, same applying to frameworks and architectures, the scenarios in which the languages are used, etc. One unrealistic expectation is that the programmers are capablle of learning a new programming language or framework overnight, this expectation pushing more pressure on programmers’ shoulders as they need to compensate in a short time for the knowledge gap. No, the programming languages are not the same even if there’s high resemblance between them!

There’s lot of code available online, many of the programming tasks involve writing similar code. This makes people assume that programming can resume to copy-paste activities and, in extremis, that there’s no creativity into the act of programming. Beside the fact that using others’ code comes with certain copyright limitations, copy-pasting code is in general a way of introducing bugs in software. One can learn a lot from others’ code, though programmers' challenge resides in writing better code, in reusing code while finding the right the level of abstraction.  

 

There’s the tendency on the market to build whole applications using wizard-like functionality and of generating source-code based on data or ontological models. Such approaches work in a range of (limited) scenarios, and even if the trend is to automate as much in the process, is not what programming is about. Each such tool comes with its own limitations that sooner or later will push back. Changing the code in order to build new functionality or to optimize the code is often not a feasible solution as it imposes further limitations.

Programming is not only about writing code. It involves also problem-solving abilities, having a certain understanding about the business processes, in which the conceptual creativity and ingenuity of design can prove to be a good asset. Modelling and implementing processes help programmers gain a unique perspective within a business.

For a programmer the learning process never stops. The release cycle for the known tools becomes smaller, each release bringing a new set of functionalities. Moreover, there are always new frameworks, environments, architectures and methodologies to learn. There’s a considerable amount of effort in expanding one's (necessary) knowledge, effort usually not planned in projects or outside of them. Trainings help in the process, though they hardly scratch the surface. Often the programmer is forced to fill the knowledge gap in his free time. This adds up to the volume of overtime one must do on projects. On the long run it becomes challenging to find the needed time for learning.

In resource planning there’s the tendency to add or replace resources on projects, while neglecting the influence this might have on a project and its timeline. Each new resource needs some time to accommodate himself on the role, to understand project requirements, to take over the work of another. Moreover, resources are replaced on project with a minimal or even without the knowledge transfer necessary for the job ahead. Unfortunately, same behavior occurs in consultancy as well, consultants being moved from one known functional area into another unknown area, changing the resources like the engines of different types of car, expecting that everything will work as magic.

Continued…

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#️⃣Software Engineering: Programming (Part XI: The Dark Side)

Software Engineering Series

As member of programmers' extended community, it’s hard to accept some of the views that inconsiderate programmers and their work. In some contexts, maybe the critics reveal some truths. It’s in human nature to generalize some of the bad experiences people have or to oversimplify some of programmers’ traits in stereotypes, however the generalizations and simplifications with pejorative connotations bring no service to the group criticized, as well to the critics.

The programmer finds himself at the end of the chain of command, and he’s therefore the easiest to blame for the problems existing in software development (SD). Some of the reasoning fallacies are equating the process of programming with programmers' capabilities, when the problems reside in the organization itself – the way it handles each step of the processes involved, the way it manages projects, the way it’s organized, the way it addresses cultural challenges, etc.

The meaningful part of the SD starts with requirements’ elicitation, the process of researching and discovering the requirements based on which a piece of software is built upon. The results of the programming process are as good as the inputs provided – the level of detail, accuracy and completeness with which the requirements were defined. It’s the known GIGO (garbage in, garbage out) principle. Even if he questions some of the requirements, for example, when they are contradictory or incomplete, each question adds more delays in the process because getting clarifying the open issues involves often several iterations. Thus, one must choose between being on time and delivering the expected quality. Another problem is that the pay-off and perception for the two is different from managerial and customers’ perspective.

A programmer’s work, the piece of software he developed, it’s seen late in the process, when it’s maybe too late to change something in utile time. This happens especially in waterfall methodology, this aspect being addressed by more modern technologies by involving the customers and getting constructive feedback early in the process, and by developing the software in iterations.

Being at the end of the chain command, programming is seen often as a low endeavor, minimizing its importance, maybe because it seems so obvious. Some even consider that anybody can program, and it’s true that, as each activity, anyone can learn to program, same as anyone can learn another craft, however as any craft it takes time and skills to master. The simple act of programming doesn’t make one a programmer, same as the act of singing doesn’t make one a singer. A programmer needs on average several years to achieve an acceptable level of mastery and profoundness. This can be done only by mastering one or more programming languages and frameworks, getting a good understanding of the SD processes and what the customers want, getting hand-on experience on a range of projects that allow programmers to learn and grow.

There are also affirmations that contain some degrees of truth. Overconfidence in one’s skills results in programmers not testing adequately their own work. Programmers attempt using the minimum of effort in achieving a task, the development environments and frameworks, the methodologies and other tools playing an important part. In extremis, through the hobbies, philosophies, behaviors and quirks they have, not necessarily good or bad, the programmers seem to isolate themselves.

In the end the various misconceptions about programmers have influence only to the degree they can pervade a community or an organization’s culture. The bottom line is, as Bjarne Stroustrup formulated it, “an organization that treats its programmers as morons will soon have programmers that are willing and able to act like morons only” [1].

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References:
[1] "The C++ Programming Language" 2nd Ed., by Bjarne Stroustrup, 1991

#️⃣Software Engineering: Programming (Part X: Programming as Art)

Software Engineering Series

Maybe seeing programming as an art is an idealistic thought, while attempting to describe programming as an art may seem an ingrate task. However, one can talk about the art of programming same way one can talk about the art of applying a craft. It’s a reflection of the mastery reached and what it takes to master something. Some call it art, others mastery, in the end it’s the drive that makes one surpass his own condition.

Besides an audience's experience with a creative skill, art means the study, process and product of a creative skill. Learning the art of programming, means primarily learning its vocabulary and its grammar, the language, then one has to learn the rules, how and when to break them, and in the end how to transcend the rules to create new languages. The poet uses metaphors and rhythm to describe the world he sees, the programmer uses abstractedness and patterns for the same. Programming is the art of using patterns to create new patterns, much like the poet does.

The drive of art is creativity independently if one talks about music, painting, poetry, mathematics or any other science. Programmer's creativity is reflected in the way he uses his tools and builds new ones. Despite the limits imposed by the programming languages he uses, the programmer can borrow anytime the knowledge of other sciences – mathematics, physics or biology – to describe the universe and make it understandable for machines. In fact, when we understand well enough something to explain to a computer we call it science [1].

Programming is both a science and an art. Paraphrasing Leonard Tippett [2], programming is a science in that its methods are basically systematic and have general application; and an art in that their successful application depends to a considerable degree on the skill and special experience of the programmer, and on his knowledge of the field of application. The programmer seems to borrow from an engineer’s natural curiosity, attention to detail, thirst for knowledge and continual improvement though these are already in programmer’s DNA.

In programming aesthetics is judged by the elegance with which one solves a problem and transcribes its implementation. The programmer is in a continuous quest with simplicity, reusability, abstractedness, elegance, time and complexity. Beauty resides in the simplicity of the code, the easiness with which complexity is reduced to computability, the way everything fit together in a whole. Through reusability and abstractedness the whole becomes more than the sum of its parts.

Programming takes its rigor and logic from mathematics. Even if the programmer is not a mathematician, he borrows from a mathematician’s way of seeing the world in structures, patterns, order, models (approximations), connectedness, networks, the designs converging to create new paradigms. Programmer's imagery conjures some part from a mathematician's art.

In extremis, through the structures and thought patterns, the programmer is in a continuous search for meanings, of creating a meaning to encompass other meanings, meanings which will hopefully converge to a greater good. It resembles the art of the philosopher, without the historical luggage.

Between the patterns of the mathematician and philosopher's search for truth, between poets artistry of manipulating the language to create new views and engineer’s cold search for formalism and methodic, programming is a way to understand the world and create new worlds. The programmer becomes the creator of glimpses of universes which, when put together like the pieces of a puzzle can create a new reality, not necessarily better, but a reality that reflects programmers’ art. For the one who learned to master a programming language nothing is impossible.

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Quotations used:
(1)“Learning the art of programming, like most other disciplines, consists of first learning the rules and then learning when to break them.” (Joshua Bloch, “Effective Java”, 2001)
(2)“[Statistics] is both a science and an art. It is a science in that its methods are basically systematic and have general application; and an art in that their successful application depends to a considerable degree on the skill and special experience of the statistician, and on his knowledge of the field of application, e.g. economics.” (Leonard Tippett, “Statistics”, 1943)

#️⃣Software Engineering: Programming (Part IX: Programmer, Coder or Developer?)

Software Engineering Series

Programmer, coder or (software) developer are terms used interchangeably to denote a person who writes a set of instructions for a computer or any other electronic device. Looking at the intrinsic meaning of the three denominations, a programmer is a person who writes programs, a coder is a person who writes code, and a developer is one who develops (makes grow) a piece of software. They look like redundant definitions, isn’t it?

A program is a stand-alone piece of code written for a given purpose – in general it’s used to transform inputs in outputs or specific actions, and involves a set of structures, libraries and other resources. Programming means primarily being able to write, understand, test and debug programs, however there can be other activities like designing, refactoring, documenting programs and the resources needed. It also involves the knowledge of a set of algorithms, libraries, architectures, methodologies and practices that can be used in the process.

Code may refer to a program, as well as parts of a program. Writing code means being able to use and understand a programming language’s instruction for a given result – validating input, acting on diverse events, formatting and transforming content, etc. The code doesn’t necessarily have to stand alone, often being incorporated inside of documents like web pages, web parts or reports.

Development of software usually means more than programing as the former is considered as a process in conceiving, specifying, designing, programming, documenting, testing and maintaining software. The gap between the two is neglectable as programming typically involves in practice the other activities as well.

Programmer and coder are unfortunately often used with a pejorative connotation. Therefore the denomination of developer seems fancier. An even fancier term is the one of software engineer, software engineering being the application of engineering to the development of software in a systematic method.

In IT there are several other roles which involve tangentially the writing of instructions – database administrator, security engineer, IT analyst, tester, designer, modeler, technical writer, etc. It looks like a soup of fancy denominations chosen expressly to confuse nontechnical people. Thus a person who covered many of the roles mentioned above, finds it sometimes difficult to define the most appropriate denomination.

A person who writes such code doesn’t have to be a programmer or even an IT professional. There are many tools on the market whose basic functionality can be extended with the help of scripts - Excel, Access, SSRS or SSIS. Many tools nowadays have basic drag and drop and wizard-based functionality which limits the need for coding, and the trend seems to move in this direction. Another trend is the building of minimizing the need for writing code to the degree that full applications can be built with drag and drops, however some degree of coding is still needed. It seems to be in demand the knowledge of one or two universal scripting languages and data-interchange formats.

Probably the main factor for naming somebody a programmer is whether he does this for a living. On the other side a person can identify himself as programmer even if his role involves only a small degree of programming or programing is more of a hobby. One can consider programming as a way of living, as a way of understanding and modelling life. This way of life borrows a little from the way of being of the mathematician, the philosopher and the engineer.

In the end is less important what’s the proper denomination. More important is with what one identifies himself and what one makes with his skills – the mental and machine-understandable universes one builds.

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#️⃣Software Engineering: Programming (Part VIII: Pair Programming)

Software Engineering Series

“Two heads are better than one” – a proverb whose wisdom is embraced today in the various forms of harnessing the collective intelligence. The use of groups in problem solving is based on principles like “the collective is more than the sum of its individuals” or that “the crowds are better on average at estimations than the experts”. All well and good, based on the rationality of the same proverb has been advanced the idea of having two developers working together on the same piece of code – one doing the programming while the other looks over the shoulder as a observer or navigator (whatever that means), reviewing each line of code as it is written, strategizing or simply being there.

This approach is known as pair programming and considered as an agile software development technique, adhering thus to the agile principles (see the agile manifesto). Beyond some intangible benefits, its intent is to reduce the volume of defects in software and thus ensure an acceptable quality of the deliverables. It’s also an extreme approach of the pear review concept.

Without considering whether pair programming adheres to the agile principles, the concept has several big loopholes. The first time I read about pair programming it took me some time to digest the idea – I was asking myself what programmer will do that on a daily basis, watching as other programmers code or being watched while coding, each line of code being followed by questions, affirmative or negative nodding… Beyond their statute of being lone wolves, programmers can cooperate when the tasks ahead requires it, however to ask a programmer watch actively as others program it won’t work on the long run!

Talking from my own experience as programmer and of a professional working together with other programmers, I know that a programmer sees each task as a challenge, a way of learning, of reaching beyond his own condition. Programming is a way of living, with its pluses and minuses.

Moreover, the complexity of the tasks doesn’t resume at handling the programming language but of resolving the right problem. Solving the right problem is not something that can one overcome with brute force but with intelligence. If using the programming language is the challenge then the problem lies somewhere else and other countermeasures must be taken!

Some studies have identified that the use of pair programming led to a reduction of defects in software, however the numbers are misleading as long they compare apples with pears. To statistically conclude that one method is better than the other means doing the same experiment with the different methods using a representative population. Unless one addressees the requirements of statistics the numbers advanced are just fiction!

Just think again about the main premise! One doubles the expenditure for a theoretical reduction of the defects?! Actually, it's more than double considering that different types of communication takes place. Without a proven basis the effort can be somewhere between 2.2 and 2.5 and for an average project this can be a lot! The costs might be bearable in situations in which the labor is cheap, however programmers’ cooperation is a must.

The whole concept of pair programming seems like a bogus idea, just like two drivers driving the same car! This approach might work when the difference in experience and skills between developers is considerable, that being met in universities or apprenticeship environments, in which the accent is put on learning and forming. It might work on handling complex tasks as some adepts declare, however even then is less likely that the average programmer will willingly do it!

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🌡Performance Management: Mastery (Part I: The Need for Perfection vs. Excellence)

A recurring theme occurring in various contexts over the years seemed to be corroborated with the need for perfection, need going sometimes in extremis beyond common sense. The simplest theory attempting to explain at least some of these situations is that people tend to confuse excellence with perfection, from this confusion deriving false beliefs, false expectations and unhealthy behavior. 

Beyond the fact that each individual has an illusory image of what perfection is about, perfection is in certain situations a limiting force rooted in the idealistic way of looking at life. Primarily, perfection denotes that we will never be good enough to reach it as we are striving to something that doesn’t exist. From this appears the external and internal criticism, criticism that instead of helping us to build something it drains out our energy to the extent that it destroys all we have built over the years with a considerable effort. Secondarily, on the long run, perfection has the tendency to steal our inner peace and balance, letting fear take over – the fear of not making mistakes, of losing the acceptance and trust of the others. It focuses on our faults, errors and failures instead of driving us to our goals. In extremis it relieves the worst in people, actors and spectators altogether. 

In its proximate semantics though at diametral side through its implications, excellence focuses on our goals, on the aspiration of aiming higher without implying a limit to it. It’s a shift of attention from failure to possibilities, on what matters, on reaching our potential, on acknowledging the long way covered. It allows us building upon former successes and failures. Excellence is what we need to aim at in personal and professional life. Will Durant explaining Aristotle said that: “We are what we repeatedly do. Excellence, then, is not an act, but a habit.” 

People who attempt giving 100% of their best to achieve a (positive) goal are to admire, however the proximity of 100% is only occasionally achievable, hopefully when needed the most. 100% is another illusory limit we force upon ourselves as it’s correlated to the degree of achievement, completeness or quality an artefact or result can ideally have. We rightly define quality as the degree to which something is fit for purpose. Again, a moving target that needs to be made explicit before we attempt to reach it otherwise quality envisions perfection rather than excellence and effort is wasted. 

Considering the volume of effort needed to achieve a goal, Pareto’s principles (aka the 80/20 rule) seems to explain the best its underlying forces. The rule states that roughly 80% of the effects come from 20% of the causes. A corollary is that we can achieve 80% of a goal with 20% of the effort needed altogether to achieve it fully. This means that to achieve the remaining 20% toward the goal we need to put four times more of the effort already spent. This rule seems to govern the elaboration of concepts, designs and other types of documents, and I suppose it can be easily extended to other activities like writing code, cleaning data, improving performance, etc. 

Given the complexity, urgency and dependencies of the tasks or goals before us probably it's beneficial sometimes to focus first on the 80% of their extent, so we can make progress, and focus on the remaining 20% if needed, when needed. This concurrent approach can allow us making progress faster in incremental steps. Also, in time, through excellence, we can bridge the gap between the two numbers as is needed less time and effort in the process.

Meta-Blogging: Mea Culpa (Part I: Changing the Status Quo)

During the past years I started multiple posts on various programming-related topics though I seldom managed to bring something close to a publishable form. The main reason seems to be the lack of time needed to put an idea into words, to look at it from different perspectives in form of a logical meaningful unit and, last but not the least, make it count. This is accentuated by the fact that each idea pulls another, and often there are so many things to say that it’s hard to find a delimitation between what to be included and what to be left out. In extremis one feels that something is missing.

Often, it's required a certain amount of research needed to validate or support the facts. The knowledge about SQL Server and other DBMS is relative – it can be only relative as long their internals are known only to a certain degree. The relativity is found also in the area of applicability, the usage of a solution over another lying in details. Readers want solid facts while all one can give is a dry “it depends”…

Unfortunately, for a blogger not found close to the source of knowledge, the content posted tends to be third or fourth-hand knowledge and, in one form or the other, just duplication of information. As long content isn’t copied and there’s some personal touch the duplication is not necessarily a bad thing. Duplication makes knowledge more likely to be found as the content is indexed by search engines, however it becomes more difficult to stand in the crowd. To bring something new one must to put existing knowledge into new contexts, to be creative, and this takes time as well.

Without access to a pool of readers and of knowledge for a lone blogger it’s hard to succeed, giving up being just a few posts or a few years away. Of course, life tends to take over. It’s also in human nature to be enthusiastic about an idea and renounce shortly with the first difficulties met. On the other side, often it’s hard to keep or to find the needed motivation, especially when there is little support coming from the blogging platforms, tools creators or content publishers. Not being able to monetize one’s effort makes blogging more of a hobby.

With small exceptions, the investments made in blogging tools are below expectations. It’s frustrating when the tools or the integration between them stopped working and there’s no simple way to overcome this. Some aspects changed with time, however blogging seems to lose in contrast with other forms of media content.

Despite the lack of time and other difficulties I want to write and share my thoughts, my experience, make the time invested in learning and solving problems count. Blogging is also a way to externalize the implicit knowledge, of sharing, of questioning some of the ideas and practices, and ultimately of getting feedback. In this resides the personal value of blogging! 

In the fight with time and words, I found myself forced to limit the length of the posts on some random nontechnical topics to 600 words. This number is rooted in the university years, representing the proximate limit of a written assignment to include an acceptable quality and coverage, and involve a bearable amount of effort. 600 is not a perfect number as its leading digit though, for the time being will do.

The challenge is to find a context to express my thoughts and experience without being too boring, without skimming through ideas. Without carrying great expectations, it’s an attempt to change the status quo! 

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#️⃣Software Engineering: SQL Reloaded (Patt II: Who Messed with My Data?)

Introduction

“Errors, like straws, upon the surface flow;
He who would search for pearls must dive below.”
(John Dryden) 

Life of a programmer is full of things that stopped working overnight. What’s beautiful about such experiences is that always there is a logical explanation for such “happenings”. There are two aspects - one is how to troubleshoot such problems, and the second – how to avoid such situations, and this is typically done through what we refer as defensive programming. On one side avoiding issues makes one’s life simpler, while issues make it fuller.

I can say that I had plenty such types of challenges in my life, most of them self-created, mainly in the learning process, but also a good share of challenges created by others. Independently of the time spent on troubleshooting such issues, it’s the experience that counts, the little wins against the “dark” side of programming. In the following series of posts I will describe some of the issues I was confronted directly or indirectly over time. In an ad-hoc characterization they can be split in syntax, logical, data, design and systemic errors.

Syntax Errors

“Watch your language young man!”

(anonymous mother) 

    Syntax in natural languages like English is the sequence in which words are put together, word’s order indicating the relationship existing between words. Based on the meaning the words carry and the relationships formed between words we are capable to interpret sentences. SQL, initially called SEQUEL (Structured English Query Language) is an English-like language designed to manipulate and retrieve data. Same as natural languages, artificial languages like SQL have their own set of (grammar) rules that when violated lead to runtime errors, leading to interruption in code execution or there can be cases when the code runs further leading to inconsistencies in data. Unlike natural languages, artificial languages interpreters are quite sensitive to syntax errors.

    Syntax errors are common to beginners, though a moment of inattention or misspelling can happen to anyone, no matter how versatile one’s coding is. Some are more frequent or have a bigger negative impact than others. Here are some of the typical types of syntax errors:
- missing brackets and quotes, especially in complex formulas;
- misspelled commands, table or column names;
- omitting table aliases or database names;
- missing objects or incorrectly referenced objects or other resources;
- incorrect statement order;
- relying on implicit conversion;
- incompatible data types;
- incorrect parameters’ order;
- missing or misplaced semicolons;
- usage of deprecated syntax.

   Typically, syntax errors are easy to track at runtime with minimal testing as long the query is static. Dynamic queries on the other side require sometimes a larger number of combinations to be tested. The higher the number of attributes to be combined and the more complex the logic behind them, the more difficult is to test all combinations. The more combinations not tested, the higher the probability that an error might lurk in the code. Dynamics queries can thus easily become (syntax) error generators.

Logical Errors

“Students are often able to use algorithms to solve numerical problems
without completely understanding the underlying scientific concept.”
(Eric Mazur) 

   One beautiful aspect of the human mind is that it needs only a rough understanding about how a tool works in order to make use of it up to an acceptable level. Therefore often it settles for the minimum of understanding that allows it to use a tool. Aspects like the limits of a tool, contexts of applicability, how it can be used efficiently to get the job done, or available alternatives, all these can be ignored in the process. As the devil lies in details, misunderstanding how a piece of technology works can prove to be our Achilles’ heel. For example, misunderstanding how sets and the different types of joins work, that lexical order differ from logical order and further to order of execution, when is appropriate or inappropriate to use a certain technique or functionality can make us make poor choices.

   One of these poor choices is the method used to solve a problem. A mature programming language can offer sometimes two or more alternatives for solving a problem. Choosing the inadequate solution can lead to performance issues in time. This type of errors can be rooted in the lack of understanding of the data, of how an application is used, or how a piece of technology works.

“I suppose it is tempting, if the only tool you have is a hammer,
to treat everything as if it were a nail.”
(Abraham Maslow) 

   Some of the errors derive from the difference between how different programming languages work with data. There can be considerable differences between procedural, relational and vector languages. When jumping from one language to another, one can be tempted to apply the same old techniques to the new language. The solution might work, though (by far) not optimal.

    The capital mistake is to be the man of one tool, and use it in all the cases, even when not appropriate. For example. when one learned working with views, attempts to apply them all over the code in order to reuse logic, creating thus chains of views which even prove to be flexible, their complexity sooner or later will kick back. Same can happen with stored procedures and other object types as well. A sign of mastery is when the developer adapts his tools to the purpose.

"For every complex problem there is an answer
that is clear, simple, and wrong."
(Henry L. Mencken) 

   One can build elegant solutions but solve the wrong problem. Misunderstanding the problem at hand is one type of error sometimes quite difficult to identify. Typically, they can be found through thorough testing. Sometimes the unavailability of (quality) data can impede the process of testing, such errors being found late in the process.

   At the opposite side, one can attempt to solve the right problem but with logic flaws – wrong steps order, wrong algorithm, wrong set of tools, or even missing facts/assumptions. A special type of logical errors are the programmatic errors, which occur when SQL code encounters a logic or behavioral error during processing (e.g. infinite loop, out of range input). [1]

Data Errors

“Data quality requires certain level of sophistication within a company
to even understand that it’s a problem.”
(Colleen Graham) 

   Poor data quality is the source for all evil, or at least for some of the evil. Typically, a good designed database makes use of a mix of techniques to reduce the chances for inconsistencies: appropriate data types and data granularity, explicit transactions, check constraints, default values, triggers or integrity constraints. Some of these techniques can be too restrictive, therefore in design one has to provide a certain flexibility in the detriment of one of the above techniques, fact that makes the design vulnerable to same range of issues: missing values, missing or duplicate records.

   No matter how good a database was designed, sometimes is difficult to cope with users’ ingenuity – misusage of functionality, typically resulting in deviations from standard processes, that can invalidate an existing query. Similar effects have the changes to processes or usage of new processed not addressed in existing queries or reports.

  Another topic that have a considerable impact on queries’ correctness is the existence, or better said the inexistence of master data policies and a board to regulate the maintenance of master data. Without proper governance of master data one might end up with a big mess with no way to bring some order in it without addressing the quality of data adequately.

Designed to Fail

“The weakest spot in a good defense is designed to fail.”
(Mark Lawrence) 

   In IT one can often meet systems designed to fail, the occurrences of errors being just a question of time, kind of a ticking bomb. In such situations, a system is only as good as its weakest link(s). Issues can be traced back to following aspects:
- systems used for what they were not designed to do – typically misusing a tool for a purpose for which another tool would be more appropriate (e.g. using Excel as database, using SSIS for real-time, using a reporting tool for data entry);
- poor performing systems - systems not adequately designed for the tasks supposed to handle (e.g. handling large volume of data/transactions);
- systems not coping with user’s inventiveness or mistakes (e.g. not validating adequately user input or not confirming critical actions like deletion of records);
- systems not configurable (e.g. usage of hardcoded values instead of parameters or configurable values);
- systems for which one of the design presumptions were invalidated by reality (e.g. input data don’t have the expected format, a certain resource always exists);
- systems not being able to handle changes in environment (e.g. changing user settings for language, numeric or data values);
- systems succumbing in their own complexity (e.g. overgeneralization, wrong mix of technologies);
- fault intolerant systems – system not handling adequately more or less unexpected errors or exceptions (e.g. division by zero, handling of nulls, network interruptions, out of memory).

Systemic Errors

    Systemic errors can be found at the borders of the “impossible”, situations in which the errors defy the common sense. Such errors are not determined by chance but are introduced by an inaccuracy inherent to the system/environment.

    A systemic error occurs when a SQL program encounters a deficiency or unexpected condition with a system resource (e.g. a program encountered insufficient space in tempdb to process a large query, database/transaction log running out of space). [1]

   Such errors are often difficult but not impossible to reproduce. The difficulty resides primarily in figuring out what happened, what caused the error. Once one found the cause, with a little resourcefulness one can come with an example to reproduce the error.

Conclusion

“To err is human; to try to prevent recurrence of error is science.“
(Anon)

    When one thinks about it, there are so many ways to fail. In the end to err is human and nobody is exempted from making mistakes, no matter how good or wise. The quest of a (good) programmer is to limit errors’ occurrences, and to correct them early in process, before they start becoming a nightmare.

References:
[1] Transact-SQL Programming: Covers Microsoft SQL Server 6.5 /7.0 and Sybase,  by Kevin Kline, Lee Gould & Andrew Zanevsky, O’Reilly, ISBN 10: 1565924010, 1999

Programming: Framework (Definitions)

"Unifying, guiding architectural approach, as in the data warehouse bus architecture." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit" 2nd Ed., 2002)

"A collection of classes, functions, protocols, documentation, and header files and other resources that are all related." (Stephen G Kochan, "Programming in Objective-C", 2003)

"A set of collaborating abstract and concrete classes that may be used as a template to solve a related family of problems. It is usually extended via subclassing for application-specific behavior." (Craig Larman, "Applying UML and Patterns", 2004)

"A coherent architecture that provides an incomplete template for systems within a specific domain; a coherent set of design patterns." (Bruce P Douglass, "Real-Time Agility", 2009)

"A support structure for developing software products." (Judith Hurwitz et al, "Service Oriented Architecture For Dummies" 2nd Ed., 2009)

"1.Generally, a basic skeletal structure. 2.Conceptually, a classification scheme used to better understand a topic; a defined and documented paradigm, used as a lens to view a complex problem. 3.In software development, a reusable object-oriented design, including a library of reusable classes and other components, along with standards for designing additional components and how they interact." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A support structure for developing and managing software products." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A structure for supporting something else." ( Manish Agrawal, "Information Security and IT Risk Management", 2014)

"A support structure for developing and managing software." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"A conceptual set of rules and ideas that provide structure to a complex and challenging situation." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed., 2015)

"A framework is a set of concepts that provide the basic structure for understanding a domain, enabling a common vocabulary for different explanatory theories." (Robert J Glushko, "The Discipline of Organizing: Professional Edition" 4th Ed., 2016)

ⵌProgramming: Assertion (Definitions)

"A constraint that is not attached to a table but is instead a distinct database object. It can therefore be used to enforce rules that apply to multiple tables or to verify that tables are not empty." (Jan L Harrington, "SQL Clearly Explained" 3rd Ed., 2010)

"A declaration or statement, often without support." (Janice M Roehl-Anderson, "IT Best Practices for Financial Managers", 2010)

"A statement about a program that the code claims to be true." (Rod Stephens, "Start Here!™ Fundamentals of Microsoft® .NET Programming", 2011)

"A statement that the code claims is true. If the statement is false, the program stops running so you can decide whether a bug occurred." (Rod Stephens, "Stephens' Visual Basic® Programming 24-Hour Trainer", 2011)

"A component of a regular expression that must be true for the pattern to match but does not necessarily match any characters itself. Often used specifically to mean a zero-width assertion." (Jon Orwant et al, "Programming Perl" 4th Ed., 2012)

"A statement about the program and its data that is supposed to be true. If the statement isn’t true, the assertion throws an exception to tell you that something is wrong." (Rod Stephens, "Beginning Software Engineering", 2015)

"A language feature used to test for conditions that should be guaranteed by program logic. If a condition checked by an assertion is found to be false, a fatal error is thrown. For added performance, assertions can be disabled when an application is deployed." (Daniel Leuck et al, "Learning Java" 5th Ed., 2020)

"A way of ensuring that a method has access to a particular resource, even if the method's callers do not have the required permission. During a stack walk, if a stack frame asserting the required permission is encountered, a security check for that permission will succeed without proceeding further. To perform an assertion of a permission, code must not only have that permission, but also be granted the SecurityPermission.Assertion permission. Unwise use of assertions can create security holes, so they should be used only with the utmost caution." (Damien Watkins et al, "Programming in the .NET Environment", 2002)

ⵌProgramming: Assembly (Definitions)

"An assembly is the unit of deployment and versioning in the .NET Framework. An assembly contains a manifest, metadata, MSIL, and possibly binary resources. Most assemblies are single files, but an assembly can consist of multiple files, such as DLLs, picture files, and even HTML files." (Adam Nathan, ".NET and COM: The Complete Interoperability Guide", 2002)

"The unit of deployment and versioning in the .NET Framework. It establishes the namespace for resolving requests for types and determines which types and resources are exposed externally and which are accessible only from within the assembly. An assembly includes an assembly manifest that describes the assembly's contents." (Damien Watkins et al, "Programming in the .NET Environment", 2002)

"A managed application module that contains class metadata and managed code as an object in SQL Server. By referencing an assembly, CLR functions, CLR stored procedures, CLR triggers, user-defined aggregates, and user-defined types can be created in SQL Server." (Thomas Moore, "MCTS 70-431: Implementing and Maintaining Microsoft SQL Server 2005", 2006)

"A managed application module, composed of class metadata and managed code, that can be embedded in a database solution as a database object in SQL Server 2005." (Marilyn Miller-White et al, "MCITP Administrator: Microsoft® SQL Server™ 2005 Optimization and Maintenance 70-444", 2007)

"Application logic that is stored in, and managed by, the SQL Server database server, including objects like triggers, CLR software, and stored procedures. Assemblies are written in a .NET language, such a C# or Visual Basic." (Robert D. Schneider and Darril Gibson, "Microsoft SQL Server 2008 All-In-One Desk Reference For Dummies", 2008)

"In SQL Server, a .NET assembly is a compiled SQL CLR executable or DLL." (Michael Coles, "Pro T-SQL 2008 Programmer's Guide", 2008)

"A managed application module that contains class metadata and managed code." (Jim Joseph et al, "Microsoft® SQL Server™ 2008 Reporting Services Unleashed", 2009)

"In .NET applications, the smallest self-contained unit of compiled code. An assembly can be a complete application, or a library that can be called by other applications." (Rod Stephens, "Start Here!™ Fundamentals of Microsoft® .NET Programming", 2011)

"The smallest independent unit of compiled code. Typically, this is a Dynamic Link Library (DLL) or executable program." (Rod Stephens, "Stephens' Visual Basic® Programming 24-Hour Trainer", 2011)

"A managed application module containing class metadata and managed code as an object in SQL Server, against which CLR functions, stored procedures, triggers, user-defined aggregates, and user-defined types can be created in SQL Server." (Microsoft, "SQL Server 2012 Glossary", 2012)

"In SQL Server, a .NET assembly is a compiled SQL CLR executable or DLL." (Jay Natarajan et al, "Pro T-SQL 2012 Programmer's Guide" 3rd Ed., 2012)

"The fundamental logical unit of managed code, consisting of one or more files containing Common Intermediate Language instructions and metadata. See also CIL." (Mark Rhodes-Ousley, "Information Security: The Complete Reference" 2nd Ed., 2013)

ⵌProgramming: Array (Definitions)

"A group of cells arranged by dimensions. A table is a two-dimensional array in which the cells are arranged in rows and columns, with one dimension forming the rows and the other dimension forming the columns. A cube is a three-dimensional array and can be visualized as a cube, with each dimension of the array forming one edge of the cube." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)

"A collection of objects all of the same type." (Jesse Liberty, "Sams Teach Yourself C++ in 24 Hours" 3rd Ed., 2001)

"A list of variables that have the same name and data type." (Greg Perry, "Sams Teach Yourself Beginning Programming in 24 Hours" 2nd Ed., 2001)

"Values whose members, called elements, are accessed by an index rather than by name. An array has a rank that specifies the number of indices needed to locate an element (sometimes called the number of dimensions) within the array. It may have either zero or nonzero lower bounds in each dimension." (Damien Watkins et al, "Programming in the .NET Environment", 2002)

"A collection of data items, all of the same type, in which each item is uniquely addressed by a 32-bit integer index. Java arrays behave like objects but have some special syntax. Java arrays begin with the index value 0." (Marcus Green & Bill Brogden, "Java 2™ Programmer Exam Cram™ 2 (Exam CX-310-035)", 2003)

"A device that aggregates large collections of hard drives into a logical whole." (Tom Petrocelli, "Data Protection and Information Lifecycle Management", 2005)

"An arithmetically derived matrix or table of rows and columns that is used to impose an order for efficient experimentation. The rows contain the individual experiments. The columns contain the experimental factors and their individual levels or set points." (Clyde M Creveling, "Six Sigma for Technical Processes: An Overview for R Executives, Technical Leaders, and Engineering Managers", 2006)

"A data structure containing an ordered list of elements—any Ruby object—starting with an index of 0." (Michael Fitzgerald, "Learning Ruby", 2007)

"An arithmetically derived matrix or table of rows and columns that is used to impose an order for efficient experimentation. The rows contain the individual experiments. The columns contain the experimental factors and their individual levels or set points." (Lynne Hambleton, "Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices", 2007)

"In a SQL database, an ordered collection of elements of the same data type stored in a single column and row of a table." (Jan L Harrington, "SQL Clearly Explained" 3rd Ed., 2010)

"A group of values stored together in a single variable and accessed by index." (Rod Stephens, "Stephens' Visual Basic® Programming 24-Hour Trainer", 2011)

"A grouping of similar items of the same storage type in a sequential pattern, and referenced by a sequential index value." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A variable that holds a series of values with the same data type. An index into the array lets the program select a particular value." (Rod Stephens, "Start Here!™ Fundamentals of Microsoft® .NET Programming", 2011)

"An ordered collection of values. Arrays can be defined as a basic Objective-C type and are implemented as objects under Foundation through the NSArray, and NSMutableArray classes." (Stephen G Kochan, "Programming in Objective-C" 4th Ed., 2011)

"A basic collection of values that is a sequence represented by a single block of memory. Arrays have efficient direct access, but do not easily grow or shrink." (Mark C Lewis, "Introduction to the Art of Programming Using Scala", 2012)

"An ordered sequence of values, stored such that you can easily access any of the values using an integer subscript that specifies the value’s offset in the sequence." (Jon Orwant et al, "Programming Perl" 4th Ed., 2012)

"A group of variables stored under a single name." (Matt Telles, "Beginning Programming", 2014)

"A structure composed of multiple identical variables that can be individually addressed." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

"A structure that contains an ordered collection of elements of the same data type in which each element can be referenced by its index value or ordinal position in the collection. See also element, ordinary array." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

"An array is a data structure where elements are associated with an index. They are implemented differently in different programming languages." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

Programming: Pair Programming (Definitions)

"An XP practice requiring that each piece of source code to be integrated into the software product should be created by two programmers jointly at one computer."" (Johannes Link & Peter Fröhlich, "Unit Testing in Java", 2003)

"A coding technique where one programmer (the driver) writes code and explains what he or she is doing, while another watches and looks for problems." (Rod Stephens, "Start Here!™ Fundamentals of Microsoft® .NET Programming", 2011)

"A software development approach whereby lines of code (production and/or test) of a component are written by two programmers sitting at a single computer. This implicitly means ongoing real-time code reviews are performed." (IQBBA, "Standard glossary of terms used in Software Engineering", 2011)

"An Extreme Programming practice where two (or three) programmers work together at the same computer. The driver or pilot types while the observer, navigator, or pointer watches and reviews each line of code as it is typed." (Rod Stephens, "Beginning Software Engineering", 2015)

"A software development approach whereby lines of code (production and/or test) of a component are written by two programmers sitting at a single computer. This implicitly means ongoing real-time code reviews are performed." (SQA)

ⵌProgramming: BLOB (Definitions)

"A type of data column containing binary data such as graphics, sound, or compiled code. This is a general term for text or image data type. BLOBs are not stored in the table rows themselves, but in separate pages referenced by a pointer in the row." (Microsoft Corporation, "SQL Server 7.0 System Administration Training Kit", 1999)

"BLOB is a data type for fields containing large binary data such as images." (S. Sumathi & S. Esakkirajan, "Fundamentals of Relational Database Management Systems", 2007)

"A binary large object. Large value data types [varchar(max), nvarchar(max), and varbinary(max)] are stored as BLOBs. Within SQL Server 2005, BLOBs can be as large as 2GB." (Darril Gibson, "MCITP SQL Server 2005 Database Developer All-in-One Exam Guide", 2008)

"A data type that can hold large objects of arbitrary content such as video files, audio files, images, and so forth. Because the data can be any arbitrary chunk of binary data, the database does not understand its contents so you cannot search in these fields." (Rod Stephens, "Beginning Database Design Solutions", 2008)

"Binary large object (BLOB) data is data that is stored using the varbinary(max) data type. A BLOB column or variable can hold up to 2.1 GB of data, as opposed to a regular non-LOB varbinary or binary column or variable, which can max out at 8,000 bytes of data." (Michael Coles & Rodney Landrum, , "Expert SQL Server 2008 Encryption", 2008)

"Very large binary representation of multimedia objects that can be stored and used in some enhanced relational databases." (Paulraj Ponniah, "Data Warehousing Fundamentals for IT Professionals", 2010)

"A discrete packet of binary data that has an exceptionally large size, such as pictures or audio tracks stored as digital data, or any variable or table column large enough to hold such values. The designation 'binary large object' typically refers to a packet of data that is stored in a database and is treated as a sequence of uninterpreted bytes." (Microsoft, "SQL Server 2012 Glossary", 2012)

"A large assemblage of binary data (e.g., images, movies, multimedia files, even collections of executable binary code) that are associated with a common group identifier and that can, in theory, be moved (from computer to computer) or searched as a singled data object. Traditional databases do not easily handle BLOBs." (Jules H Berman, "Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information", 2013)

"A blob is any resource whose internal structure is functionally opaque for the purpose at hand." (Robert J Glushko, "The Discipline of Organizing: Professional Edition" 4th Ed., 2016)

ⵌProgramming: Block (Definitions)

"A series of statements enclosed by BEGIN and END. Blocks define which set of statements will be affected by control-of-flow language such as IF or WHILE. You can nest BEGIN...END blocks within other BEGIN... END blocks." (Microsoft Corporation, "SQL Server 7.0 System Administration Training Kit", 1999)

"A section of code grouped together by braces that sets apart a section of code in a smaller area than a full procedure. A procedure might contain several blocks of code." (Greg Perry, "Sams Teach Yourself Beginning Programming in 24 Hours 2nd Ed.", 2001)

"A sequence of PL/SQL code, beginning with DECLARE or BEGIN and ending with END. The block is a core organizational unit of PL/SQL programming. See Chapter 2 for a thorough discussion." (Bill Pribyl & Steven Feuerstein, "Learning Oracle PL/SQL", 2001)

"A series of Transact-SQL statements enclosed by BEGIN and END. You can nest BEGIN...END blocks within other BEGIN...END blocks." (Anthony Sequeira & Brian Alderman, "The SQL Server 2000 Book", 2003)

"A syntactic construct consisting of a sequence of Perl statements that is delimited by braces. The if and while statements are defined in terms of BLOCKs, for instance. Sometimes we also say 'block' to mean a lexical scope; that is, a sequence of statements that acts like a BLOCK, such as within an eval or a file, even though the statements aren’t delimited by braces." (Jon Orwant et al, "Programming Perl" 4th Ed., 2012)

"A Transact-SQL statement enclosed by BEGIN and END." (Microsoft, "SQL Server 2012 Glossary", 2012)

"The information stored in a sector" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)

"A set of rows retrieved from a database server that are transmitted as a single result set to satisfy a cursor FETCH request." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

#️⃣Software Engineering: Programming (Part VI: What is Programming About?)

Software Engineering Series

According to Wikipedia, computer programming (shortly programming or coding) is “the process of designing, writing, testing, debugging, and maintaining the source code of computer programs”. That’s an extensive definition, because typically programming refers mainly to the writing of a set of instructions understandable by a computer or any other electronic device. At least that’s what programming was at its beginnings. 

With time, giving the increasing complexity of software, programming included also activities like gathering requirements, architecting, designing, testing, debugging and troubleshooting, refactoring, documenting, configuring, deploying, performing maintenance, etc. Each of these activities comes with their own set of methods, procedures, processes, models, methodologies, best/good practices, standards and tools. In addition, when we look at the architecture of an application, we can delimit several layers: server vs. client, front-end (user interface), business layer, backend (database), transport (network), communication or hardware – they coming with their own set of technologies and knowledge luggage, and requiring some specialization too.

However, making abstraction of all these, programming implies the (partial) knowledge of a programming language, an artificial language used to communicate with machines, in terms of language syntax, semantics and built-in libraries, and of a IDE (Integrated Development Environment), an application in which the code is written, compiled/interpreted and debugged. As programming can be often a redundant task, being necessary to solve the same kind of problems or to write the same kind of instructions, in addition to the various structures and techniques made available in order to minimize redundancy, a programmer can take advantage of a huge collection of algorithms, abstracted step-by-step instructions, and afferent technical literature. The deeper needs to go their understanding, the broader the set of knowledge to be acquired for it.

And even if we consider all above, that’s not enough because programming is used in order to model and solve business-specific problems. So is required some minimal knowledge of the respective business domains, and that’s quite a lot if we consider that each project may address one or more business domains. Talking about projects, as most of the programming is performed within projects, a programmer needs to have some knowledge of the procedures, methods and methodologies for project management and team management. That’s not programming anymore, but it’s part of the landscape and nowadays is kind of a must because programming is performed within projects and teams. This means also that a programmer needs to cover several important interpersonal skills, to which add up customer oriented, social and thinking skills. They are important because they impact directly or indirectly the act of programming, and many ignore this.

It’s important to stress that programming is not only the knowledge of languages, algorithms, tools, methods, models, practices, methodologies, standards, but also their adequate use in order to make most of the programming experience. Or as a long time ago retrieved quotes puts it: “programming is 10% science, 20% ingenuity, and 70% getting the ingenuity to work with the science” (anonymous). We all (or almost all) master our native language to the degree of writing sentences or communicating, though it takes skills to communicate effectively and efficiently, or of making from language a tool of expression through poetry or other forms o literature. Fantasizing a little, programming is like writing poetry, is one thing to write chunks of words, and another thing to write something meaningful. And programming is a lot about interpretation and representation of meaning in order to solve problems, is about understanding and breaking down complexity to a level that can be translated in meaning to machines. 

Programming is an art, to the same degree each endeavor can be transformed in art. It requires skills, knowledge, dedication, creativity, and most of all the pleasure of programming. Programming is a state of spirit, is a way or model of thinking, of seeing the whole world in computable terms.

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