A part of my job is to debate with people about agile software development, sometimes, just analyzing discussions without taking part in it. I noticed one major factor that leads to arguments: misunderstanding. Two people may not agree on something only because they are simply not talking about the same thing, even though they are using the same words! In fact, words correspond to very different concepts or experiences in people. When the description is made using something that is unknown to us, we will first try, consciously or unconsciously, to link its properties to what we are already familiar with, and not necessarily take the required step back to interpret it globally by disregarding our existing knowledge. This understanding of things is partially or totally dependent on the subjective perception of the participants in a discussion. These perceptions are largely molded by the different cognitive biases that interact, self-reinforce. In psychology, we speak of heuristics in judgment. All of this can become very problematic in the context of so-called “psychological contracts,” where complexity emerges from these variable perceptions because of the positive (or negative) behavioral loops that become impossible to solve.

The debates can take long hours before we actually notice the problem, but in many cases, it remains unnoticed and everyone leaves with his or her beliefs and nothing changes. The fact that this article (and many others) exist is the consequence that there is widespread confusion caused by the creation of new terms to define similar (or existing) roles or behaviors. For example, the term “Carnism” or “Cisgender” create similar confusion and unnecessary division that prevents people from understanding each other.  Therefore, in some domains, knowing the good terms is so important that you don’t get your diploma if you don’t master them, such as in health or aeronautics. In some areas, using the right term is a matter of life and death.

What is the relationship with software development? The reason is that semantics are fundamental in the day-to-day work of knowledge workers and this is often the case with failed or poorly understood Scrum implementation. This goes from the very definition of a product owner to more fundamental things. But what motivated this post, besides my own observation of the problem (in many other contexts, as well), is the discovery that this phenomenon was much more widespread in the software industry than I thought and that it tended to annoy some (and that’s the least we can say).

In a recent blog post, Gregg Caines put it more gently, but very well:

When you want to get people to change the way they work, and you want them to understand the completely foreign concepts you’re bringing to them, it’s absolutely crucial that you name the thing in a way that also explains what it is not.

He continues with an example:

In Scrum, it’s also common to have a “sprint commitment” where the team “commits” to a body of work to accomplish in that time frame. The commitment is meant to be a rough estimate for the sake of planning purposes, and if a team doesn’t get that work done in that time, it tries to learn from the estimate and be more realistic in the next sprint. Developers are not supposed to be chastised for not meeting the sprint commitment — it’s just an extra piece of information to improve upon and to use for future planning. Obviously naming is hugely important here too, because in every other use of the word, a “commitment” is a pledge or a binding agreement, and this misnomer really influences the way people (mis)understand the concept of sprints. Let’s face it: if people see sprints as just more frequent deadlines (including those implementing them), the fault can’t be entirely theirs.

It is highly conceivable that Scrum defines new terms to impose a new way of thinking, as well (Agile Mindset). Ironically, this requirement is perhaps what contributes to the numerous failures of implementation of the framework, but also, and more importantly, to the proliferation of alternatives which infuriates its creator.

The lack of centralized and detailed information about Scrum can also contribute to the problem. The official scrum guide, with only a few dozen pages, is as highly subject to interpretation as your astrological sign of the day. The underlying psychological phenomenon for both is exactly the same:  it’s called the Barnum effect. It’s the tendency to interpret a general text to be specific to us. When it affects us, we filter the information and make it “match” our beliefs or knowledge, as I mentioned in the introduction. Sometimes it goes very far and I have already seen people develop theories about the functioning of the human brain based on sacred writings. This really makes me sad because the creator of Scrum probably agrees with me as they introduced transparency in the three pillars of Scrum along with inspection & adaptation:

Significant aspects of the process must be visible to those responsible for the outcome. Transparency requires those aspects be defined by a common standard, so observers share a common understanding of what is being seen.

OK, so far so good, but who benefits from the crime? Probably the agile consultants. It is possible for many of them to fill the electricity needs of an entire city simply talking in front of a wind farm. But how much does it cost? If I only rely on my experience to answer, I can attest that three generations of highly paid coaches are sometimes needed to make an organization understand agile fundamentals. Having been a “confluence archaeologist” several times, I can also attest that in some cases, it could never have been otherwise if the definition from my predecessors I read was the one submitted to the top management. They are not helped by the terms they need to promote.

That’s why I’m in favor of calling a spade a spade. Product Manager is the term everybody understands. It generally covers everything a “Product Owner” is supposed to do. I have the exact same opinion for the “Scrum Master” role (the “Servant Leader”), which creates further confusion, especially in different languages where the term servant is taken literally, and often leads to a “Scrum Janitor” or worse, a “Scrum Manager” in badly implemented Scrum. The guide mentions that it is simple to understand. I object. It also mentions that is it difficult to master. I agree. But one of the factors is definitely its inappropriate terms that create a breeding ground for misunderstanding. Therefore, a brave act would be to redefine the terms that would lead Scrum to the next level.

The Sprint Goal, as defined by the creators of Scrum, is a selection of product backlog items that form one coherent feature, which helps a development team to work together rather than pursue separate initiatives. Ian Mitchell, a writer on agile software development, summarizes the usefulness of the Sprint Goal by writing the following statement on a blog post:

What we are getting at here is that the whole purpose of a sprint is to meet a Sprint Goal, and Scrum is framed to support sprint-based delivery. Take away the Sprint Goal and the rationale for using Scrum disappears with it. You might as well operate on the basis of continuous flow. Without a Sprint Goal to meet, sprint backlogs can serve no useful purpose.

The Sprint Goal is a description of the coherent increments that will be delivered at the end of the sprint and it provides guidance to the development team on why it is building an increment and how it should do so. If the development team discovers that they won’t be able to deliver that coherent feature during the sprint, they can negotiate the scope with the PO.

We can go even further by describing all the advantages of a well defined Sprint Goal and how it helps to get things done effectively. It helps …

• The team to understand the purpose of their work beyond the description of individual PBIs, which stimulates intrinsic motivation and compliance. This leads to better implementation as the team can keep the big picture in mind. A well known man built an empire on this concept alone.
• To keep the team focused on a small objective and avoid the negative impact of context switching. It can be seen as mini projects. These are clearly achievable multiple goals instead of a big one that is divided into chunks used to fill sprints (Waterfall disguised as Scrum). Focus is a very important concept to understand when you work with knowledge and creative workers (in the case of the vast majority of people in software development); it is probably the most important thing to work on in order to deliver value and quality to your customers.
• The team to collaborate on a single objective with required flexibility and empowerment (cohesion vs individualism). This leads to better shared responsibility as the success of the sprint will be evaluated based on whether the goal has been met, instead of counting all the individual tasks that can lead to individual developer comparison in the worst implementation of Scrum. This helps in getting cohesion which is the most important factor for a high performing team.
• The PO provide clear value beyond the selected product backlog items (PBI) and therefore improve prioritisation. This helps getting a realeasable product increment at the end of each sprint, which is the whole point of agile software development.
• The PO to communicate on progress based on working software, instead of a percentage of a list of individual tasks that means nothing to the customer. This also helps to create a valuable roadmap to communicate to users.
• The PO to get structured feedback from each meaningful increment instead of individual tasks. This provides the next sprints with new insights which automatically leverage the power of Scrum or any other iteration based framework.

The Sprint Goal, when properly used, is a proof that Scrum is understood and correctly implemented. Together with the (potential) release of a new product increment, this makes the Scrum Sprint really useful. Therefore, this is the first thing I check when I’m asked to audit an existing Scrum implementation. Sadly, this is the most overlooked artifact of Scrum, so if you begin a new implementation, start with the sprint goal and explain why it’s useful.

It may surprise some, but my interest in cognitive sciences was initiated by the bullshiters I frequently encountered in seminars, meetings and other events related to my business sector (entrepreneurship, creativity and computer science). I was bombarded with countless theories and concepts, and more importantly organisational ones, each one more ingenious than the other and which seemed able to solve anything. All these theories were of course backed up by so called “scientific” articles. Despite this evidence seen as irrefutable by their authors, I was wary of this “silver bullet” that was regularly marketed to me, sometimes advertised simply as such. In fact, some of these poorly argued theories sparked my interest more than others. On the one hand, some of the proposed arguments annoyed me by being inappropriate, and on the other, I felt that some theories were more credible than others, but poorly addressed or interpreted. Of course, at the time, I was not aware that my own reasoning based on my intuition was not appropriate, but I still felt that there was something to be done in this area, without really knowing what. A common trait in all these “stories” was that the “evangelists” (a regularly used term) of these methods seldom seemed to really master their subject, in my opinion. All that seemed important to them was to sell their ideas at any cost, including bulling, i.e. not actually lying, but using enumerations in keeping with the theory, to better convince their audience (Perry, 1963). Using reference philosophical texts on the epistemology of cognitive sciences, I will try to make the connection with the methods used in the Lean Startup, still very popular today, and try to explain the risks of such an approach.

Already in 1904, the geologist Thomas C. Chamberlin approached the topic of methodology in scientific research, in his article “The Methods of the Earth-sciences” (Chamberlin, 1904). According to him, the best method to verify a hypothesis is the one that is most precise, but also exhaustive and unbiased. To avoid this last risk, the researcher (or entrepreneur looking for a business model) must make his or her observations without any preconceptions. Without development, this approach is problematic as it prevents the researcher from detailing an observation to obtain relevant data. As Karl Popper (1969) demonstrated to his students, the simple instruction “observe” naturally leads to the following questioning: “What should be observed?” The choice of our subject of interest therefore arises from a starting hypothesis which arose from one or more observations. Moreover, a lot of our knowledge is built using the hypothetico-deductive model, as described by Bacon (1268). This procedure consists of first formulating a hypothesis, deducting its consequences and then testing them using experiments. Chamberlin (1904) proposed in opposition to “The Method of Colourless Observation” which has just been described, two other methods called “The Method of the Ruling Theory” and “The Method of the Working Hypothesis“. The first consists of carrying out as many experiments as needed to verify a first hypothesis. The second is to consider a hypothesis as tentative until observations contradict it. This is the method recommended in the business startup stimulation programs (including those in which I actively participate).

According to Chamberlin, this latter method can often degenerate into the first one if the experimenter is not vigilant. In both cases, a theory can be compared to the researcher’s child. It is easy to imagine how some people persist, like a magistrate who examines exculpatory and not always incriminatory evidence, a prisoner of his own beliefs and convictions, and only retains information that confirms his hypothesis, neglecting everything else. Because elaborating a theory based on a series of empirical observations, however numerous, confirming the underlying hypothesis, is not without a whole series of difficulties, including the problem of induction (Popper, 1969). Induction consists of a mental operation through which we go from given observations to a proposal that explains it. This method is therefore probabilistic since it is based on the principle that the greater the number of observations, the greater the likelihood that the general theory deduced from the latter be true.

However, according to Karl Popper (1969), no general theory can be justified using this method since most of the time it’s impossible to observe all instances of a type of observable facts since induction can only be justified by induction, which causes an infinite regress. Popper did not hesitate to characterize the generation of hypothesis by induction a myth in the development of objective scientific knowledge. His first argument concerns the selective nature of the observation. To observe, attention must be focused on a point of interest. This point of interest originates from a hypothesis that has been constructed from one or more observations. Which makes it a recursive process. This tendency to look for regularities in the world around us is a very powerful learning mechanism on which conditional reasoning is based. It seems natural, but can pose a whole new series of problems…

This method concerns multiple cognitive biases, of which the best known is the “confirmation bias“, the powerful effects of which have been demonstrated by Wason & Johnson-Laird (1972). Wason’s original experience (1960) suggested that participants discover a rule from the sequence of starting digits “2-4-6”. Participants could propose sequences of figures to test their hypotheses. They were then told whether this suite was compatible with the rule to be discovered.  After making several proposals, they were then asked to formulate a general rule, of which they were absolutely certain. A positive or negative feedback followed. This task uses our ability to generate hypotheses but also the ability to give up. According to the author, these two processes would interact. The experiment was created to highlight the method most commonly used by the participants to find the rule. It also highlights the fact that most of the participants are prepared to formulate a rule based solely on proposals that correspond to their initial hypothesis. Most subjects proposed the sequence of numbers “4 – 6 – 8” (valid), then “6 – 8 – 10” (also valid) to prematurely announce the rule “two is added to each next digit“. In fact, the rule is “figures in ascending order” and only barely 21% of the participants found it without having an erroneous solution. More surprisingly, more than 51% of the proposals, with an erroneous solution, remained consistent with the latter. Most people, even if very intelligent, seem incapable of considering that there may be a different or more basic rule than the one that seems to work (Miller, 1967). Where most participants used the strategy to verify their hypothesis, the others tended to invalidate it using proposals such as “2 – 4 – 10” (valid) or “10 – 6 – 4” (invalid) or “1 – 7 – 13” (valid). As Popper (1969) stated, there is no “valid” inductive method since it is impossible to guarantee that a generalization from observations confirming a hypothesis, be correct.

A second major problem appears with the “always correct” theories that nothing seems able to dispute. These theories are often referred to as pseudoscience, due to this characteristic and one of the most commonly used examples to illustrate it in scientific psychology is psychoanalysis. Thanks to concepts such as ambivalence, resistance, repression or denial, it is always possible to interpret a behaviour with hindsight. A set of behaviours can therefore go in one direction or in the opposite direction without ever questioning the theory. It is these peculiarities that make psychoanalytic theories difficult to experimentally test, giving them longevity (in the case of French speaking countries) and the high progressiveness in their decline contrary to scientific theories that disappear abruptly when they are proven to be false backed up by new data (Meehl, 1978). For Popper (1969), a theory is only scientific if it is falsifiable. This falsifiability would be the demarcation criterion between a scientific theory and one that would not be. The latter formalised, at the end of the 1920s, his conclusions as follows (slightly adapted by myself):

1. It is easy to obtain confirmations, or verifications for almost every theory, if confirmation is being looked for.
2. Confirmations should only be considered if they are the result of risky predictions; that is to say, we should have expected an event which was incompatible with the theory and that would have refuted it.
3. Every “good” scientific theory is a prohibition: it forbids certain things to happen. The more a theory forbids, the better it is.
4. A theory which is not refutable by any conceivable event is non-scientific. Irrefutability is not a virtue of a theory (as people often think), but a vice.
5. Every genuine test of a theory is an attempt to falsify or refute it. Testability is falsifiability; but there are degrees of testability; some theories are more testable, more prone to refutation, than others; they take as it were, greater risks.
6. Evidence confirming the theory should not count except when it is the result of a genuine test attempt; which means that it can be presented as a serious but unsuccessful attempt to falsify the theory. (I speak in such cases of “corroborating evidence“.)
7. Some genuine testable theories, when found to be false, are always upheld by their admirers, for example by introducing ad hoc an auxiliary hypothesis or by re-interpreting the theory in such a way that it escapes the refutation. Such a procedure is always possible, but it rescues the theory from being refuted at the cost of destroying, or at least lowering its scientific status. (I later describe such a rescue operation as a “conventionalist twist“).

Consequently, to increase the quality of a theory, it is necessary, not to increase the number of empirical observations that confirm it, but to actively seek events that make it impossible, such as the alibi of an alleged killer who prevents him from being physically at the scene of the crime at the time it occurred. Researchers (of a business model) must therefore formulate their theory by making clear how it can be refuted, and not the means by which it can be confirmed. Chamberlin (1904) proposed to use the method that he called “The Method of Multiple Working Hypothesis” which implies the elaboration, even before experimentation has begun, of multiple hypotheses, some of which contradict themselves.  So, the researcher must have an open mind and remain open to all possible interpretations of a studied phenomenon, including the possibility that none of the explanations are correct, and that even some new explanations may appear.

This latter effect, more than desirable, is also compatible with the social sciences (in entrepreneurship), which often suggests that behaviour originates from multiple causes that can interact with each other and thus make the repetition and use of statistical tests complicated. This is the major problem in cognitive science. Meehl (1978) does not hesitate to declare that, according to him, the field of research in psychology (clinical, social, etc.) based on correlations and tests of variance is so problematic, that it is scientifically irrelevant. In his original article, Meehl detailed the 20 reasons that led him to declare that Popper’s method (1969) was incompatible with statistical tests such as variance testing and analysis. He declared no more, no less that “The almost universal dependence of the rejection of the null hypothesis is a terrible mistake, is fundamentally unhealthy, is a bad scientific strategy, and is one of the worst things that ever happened in the history of psychology“. To illustrate his point, Meehl states that the variability of psychological processes is so highly dependent on context, that it is impossible to recognise an interesting and relevant meaning outside the experimental environment designed by the researcher. To draw up a list of these parasitic variables is as impossible as quantifying the precise effects on a given dependent variable, not to mention that the samples used are often far too small. He goes even further by questioning meta-analysis aimed at linking studies that study the same psychological construct and to draw a general conclusion on its validity. For Meehl, researchers in psychology who wish to highlight the explanation of a theoretical construct, should prefer consistency testing to significant statistical tests, i.e. use different means and not redundant estimations of a quantitative value for this construct. He concludes by declaring that it is the very nature of certain fields of study in psychology, such as social psychology or differential psychology, which are not compatible with the suggested method and are probably doomed to never have any major theory, scientifically speaking.

Considering everything mentioned previously, it should be noted that in social sciences (and therefore including entrepreneurship), many questions can be satisfied with dichotomous answers such as “Is this one better than the other?“. So, the question is not “what is scientific or not?“, or “what is the best method?“, but “what are the most appropriate tools to achieve this or that objective?” The entrepreneur’s is to use the appropriate tools for their research object and above all to correctly master them to avoid any erroneous interpretation of the results of their experiments, which could, in this precise case, lead to their loss with serious financial consequences. If the significance test was heavily abused in some areas of psychological sciences, to the detriment, for example, of the size of the effect test, is probably due to the way of funding research that drives the most brilliant theorists to publish or perish. The entrepreneur, very often put their own money, or their time, with no certainty of remuneration. Not addressing these issues seriously comes down to the same as putting all your eggs in one basket.

In conclusion, the entrepreneur (or professional researcher) must show intellectual honesty and be as rigorous as their role entails, even if in order to do so, they have to oppose the standards established by the markets (or institutions) in which they evolve. Paul E. Meehl (1989) said, in his famous courses on psychological philosophy the following: “If you get to a point where you say to yourself ‘I will cling to this blasted theory, whatever happens, come hell or high water’ you are no longer respecting the rules of scientific research”. The entrepreneur then enters the vicious circle of the optimistic entrepreneur where hope is fed by the information filtered through confirmation bias. And that’s exactly what you need to avoid at all costs.

References

Bacon, R. (1268). On Experimental Science.

Chamberlin, T. C. (1904). The methods of the earth-sciences. Popular Science Monthly, pp. 66:66-75.

Eccles, J. G. (1992). Under the Spell of the Synapse. In F. Worden, J. Swazey, & G. Adelman, The Neurosciences: Paths of Discovery, I (pp. 159-179). Boston: Birkhäuser Boston.

Meehl, P. E. (1978). Theoretical ricks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progressof soft psychology. Journal of Consulting and Clinical Psychology, pp. 46:806-34.

Meehl, P. E. (1989). Paul E. Meehl Philosophical Psychology Videos. Retrieved from Departement of Psychology of the University of Minnesota: http://www.psych.umn.edu/meehlvideos.php

Miller, G. A. (1967). The Psychology of Communication. New York: Basic Books.

Perry, W. G. (1963). Examsmanship and the Liberal Arts. In Examining in Harvard College: a collection of essays by members of the Harvard faculty (p. Examsmanship and the Liberal Arts.). Cambridge, MA: Harvard University.

Popper, K. (1969). Conjectures and Refutations: The Growth of Scientific Knowledge. Routledge.

Wason, P. C. (1960). On the failure to eliminate hypotheses in a conceptual task. Quarterly Journal of Experimental Psychology, 12(3), 129-140. doi:10.1080/17470216008416717

Wason, P., & Johnson-Laird, P. (1972). Psychology of Reasoning: Structure and Content. Cambridge: Harvard University Press.

Entrepreneurs are often trapped in a vicious circle of hope.  Hope clouds judgement and can be what prevents the entrepreneur seeing things clearly and taking the appropriate decisions.  Hope is so seducing that it’s what is used in most personal development or “get rich in x lessons” books.  Hope is also so powerful and rewarding that it is fed by proofs generated by the confirmation bias, that is itself fueled by hope. Entrepreneurs must learn its underlying concepts and learn to get objective opinions from others.

Confirmation bias refers to a type of selective thinking whereby one tends to notice and to look for what confirms one’s beliefs, and to ignore, not look for, or undervalue the relevance of what contradicts one’s beliefs.  [Skeptics Dictionnary]

Before talking about confirmation bias, let’s understand what is hope from a psychological point of view.  Hope is one of the many mental defense mechanism we have that is triggered in order to disconnect us from hurtful emotions (anxiety, sadness, despair, ….).  It uses thoughts to construct a positive scenario of the future.  People often grab these thoughts as a life buoy to avoid the reality of the present moment. We usually hope for a better future when we are  uncomfortable with the present.  Hope is what drives many wantrepreneurs.  Hope will make entrepreneurship’s book writers rich, not you.  Happy people hope for the best, once, then stop thinking about it.

In order to fuel hope, you need proofs that what you see in the future is possible and is likely to happen. This is when the confirmation bias enters into action. Every single proof you see that makes your predictions credible is highlighted, while every single piece of evidence that it won’t is denied. Those proofs stimulate hope that itself forces you to suffer from the confirmation bias. It’s a vicious circle.  One great example of the relationship between hope and the confirmation bias is seen with believers of the 2012 end of world event.  They can point to you dozens of  scientific  studies that prove it will happen, ignoring the hundred other proofs that it won’t.  One seducing thought in the 2012 case is that the event can potentially make them better than they are now.  Hope is triggered by a desire for change and fueled by confirmation bias.  Desire for change is not the only way to trigger hope.  Any unwanted emotion, such as fear, can also be a very good motivator: some religions claim that if you are not a good practitioner, you won’t go to paradise, but burn in hell forever. With entrepreneurs the desire for change is the key to the process.

Conscious thinking is not the only factor: things get worse when we take into consideration theories of biological psychology.  Desire to change is not the only motivator for hope. There is a physiological reward for the behavior. Dr. Robert Sapolsky, professor of biology and neurology at Stanford University conducted experiments that showed that there is a direct link between hope and dopamine (pleasure hormone) releases in the brain.  Studies even show that we get higher dopamine releases when there are more uncertainties. Uncertainties? That is certainly something that entrepreneurs can relate to.

The ability to cope with temporary difficulties is one of the entrepreneur’s required abilities. Too many people can’t go through what Seth Godin called The Dip.  Defeatism, the opposite of hope, is one of the obvious failure factors in entrepreneurship.  Often they decide to stop.  They think their efforts are not worth it anymore.  Defeatism works just like hope. Your judgment is biased and you see things from the negative side.  It is also fueled by the confirmation bias in the same way.

Optimism is often suggested as a strategy to fight defeatism.  It’s true that if you tend to be defeatist, hope can counter balance the feeling by replacing negative thoughts by positive ones.  It’s positive psychology. And it’s even suggested by the Emotional Intelligence guru Daniel Goleman.

Having hope means that one will not  give in to overwhelming anxiety, a defeatist attitude, or  depression in the face of difficult challenges or setbacks. [Daniel Goleman]

Just as defeatism must be avoided, hope can’t be a strategy on the long term. It can even  hurt as much as defeatism.  Do you remember how you felt after you really hoped that something would happen and it did not? You would be really surprised if you noted each prediction you make and compare them with what actually happened.  Hope will play against you.  It will hurt, it will hide a more concrete problem, and more importantly, it will bias your judgment.

Self awareness, again, is the key. To manage the process, consider hope (or defeatism) as a signal you must decode by being aware of the physical and psychological mechanisms. If we suspect we are biased, we must not believe entirely what we think and assess every hypothesis we make with realistic information.  Market studies and/or customer development are ways to assess our assumptions, as well getting mentorships from more experienced people who have learnt the hard way. With some practice, you will be able to acquire the self awareness required to avoid being trapped in those loops. Be a mindful hacker.

There are two common types of behavior I have noticed in people regarding their competition. You have the competition driven people, and the competition denial people .

The first constantly monitor their competitor’s activity by visiting their websites & forums or googling them constantly, while the latter meticulously try to avoid any piece of text mentioning their names.  Sometimes, the latter behaves like the former by accident, triggered by some source of information they have read by mistake… Both emotionally driven behaviors are very dangerous for their business.

A third widespread behavior, linked to the same concept, is to not enter a market because of the presence of one or more competitors or even worse, entering a market without studying the competition in detail. All these types of behavior will make your business decisions weaker. The solution is to take your competition for what it is, no more, no less and try to intelligently decode your emotions and thoughts (both are linked).

When someone is competition driven, he will often take any of the competition’s initiatives as something he doesn’t have (and therefore he must have), instead of taking it for what it is: an initiative that can be equally good or bad.  The resulting action is to try copying the competitor’s ideas (but perhaps doing them better…). The problem with that behavior is that the competition will always be significantly in advance and the market will inevitably perceive him as the follower, not the leader, constantly competing on basic stuff such as features & pricing.  It’s not only his innovation that will be affected, but his overall entrepreneur’s ability to take good decisions.  Being so abnormally obsessed by a competitor can be the start of very serious trouble possibly leading to burnout or worse, to the abandonment of the venture.  His fear of competition is irrational.  Many of his important decisions will be biased by his distorted perception of his challengers.  It’s not how he should develop a wealthy company.

Competition denial on the other hand, is the action of ignoring the competition completely. This is a very commonly suggested strategy to solve the results of competition driven behavior. That is, by ignoring anything the competition do, his judgment, innovation & decisions are not affected by what they are doing. After all, he may say, listening to the market and customers is the only valuable thing to do.  Instead of being competition driven, he becomes customer driven.  It’s a seducing way to drive your business, but ignoring the opponent can hurt you as much as being obsessed by them.

If you know the enemy and know yourself, you need not fear the result of a hundred battles.  If you know yourself but not the enemy, for every victory gained you will also suffer a defeat.  If you know neither the enemy nor yourself, you will succumb in every battle.

Sun Tzu, The Art Of War

This is something I learnt from practicing martial arts. Before an important fight, you must watch videos of your opponents previous battles (or previous fights in a competition).  Watch how he moves, what he is good at, what he sucks at.  It will helps you develop effective ways to attack him and defend yourself.  Strategy is not the only important thing in a battle.  Attitude can make all the difference.  If you have a very determined opponent in front of you, this will affect your morale during the battle, and therefore your performance.  Invest in strategy by analyzing the market, including competitors, and in self confidence.  The former will help your differentiation in the market.  The later will surely contribute to making your competitor fear you, leading them to one or both of the unwanted behaviors described in this post, and making you the leader. Maybe.

There is a difference of judgment in any activity depending if we are an actor or an observer (Malle, Knobe, Nelson 2007). I realized this fact when I was practicing martial arts, long before my studies in psychology. The audience, usually parents, downplayed the difficulty of the discipline and very often judged the activity incorrectly. One of the most common expressions was, “but it is only dancing.”

This comment annoyed many of the new practitioners, but not the more experienced who knew that this was a gross misjudgement. You could see the difference when these same people decided to try the discipline once. As you can imagine, most did not usually go beyond the first attempt as it was physically demanding and much more difficult than they had imagined. Their judgement of the activity observed radically changed when they became an actor.

In our everyday life, we alternate between the positions of observer and actor. In both situations, we make judgments and many of the various decisions we take are based on them. As an observer, we can judge incorrectly the behavior of others. As an actor, we take into account the judgment of observers. It is important to recognize these situations and act accordingly.

Let’s take a concrete example: starting a software business. This is a subject dear to my heart as I far too often watch the growing disillusions of entrepreneurs when they take in the reality of things. As the disillusionment grows, only those predisposed (very rare) or those working for pleasure keep going.

The reality is that entrepreneurs work very hard and take on tasks that we would probably never have agreed to do as an employee.  You must also be aware that very few projects succeed at first; most stop after the first failure.

When we see talented entrepreneurs in the newspapers, we think it’s easy and as simple as registering a domain name, writing 10 lines of code and then selling your company for millions. The reality is that most successful people have worked hard, often in physical and psychological pain, for many years and have faced all kinds of problems. They made the difference by persevering. All my successes have been preceded by hard work and pain.

It is difficult to realize this when you have not been there yourself. But how do you know before you make a judgement or take an important decision?

The first step is certainly to become aware of any bias and take it into account. It is very difficult as these behaviors are unconscious and judgment is rooted in how we operate.

When you realize that you are in an observer’s position and about to judge and eventually take a decision, you should not be satisfied with the information available. As you may have noticed, observing is insufficient to get an opinion, even if one is aware of the bias. Failing testing it yourself (and you can’t start a business as a test in the same way you can attend a single martial art class), the only solution is to ask questions of the actors. Those who have experience in the field or are in the situation. Do not just question one of them. The more people you question, the more relevant your information.

The only concern with this approach is that it can block you from moving forward. Indeed, if you ask too many questions you can start to stagnate. Everyone now knows that one of the primary qualities of an entrepreneur is his ability to move forward quickly. Many are also characterized by a certain impulsive trait which will be discussed in a future article.

Awareness of the actor / observer asymmetry is directly related to critical thinking: identifying biases, separating fact from opinion and analyzing data. Awareness of our mental functioning is, again, the key.

The estimate of the value of production of software based on the number of lines of code (LOC or KLOC or SLOC) is as popular as it is controversial. The main criticism is that there are too many factors influencing the final measurement value. Robert E. Park (1992, page 140), software metrics specialist & staunch defender of the method, responded to critics with the following:

“When we hear criticism of SLOC as a software measure, we are reminded of a perhaps apocryphal story about a ditch digger who, when asked one day how he was doing, replied, “Dug seventeen feet of ditch today.” He didn’t bother to say how wide or how deep, how rocky or impenetrable the soil, how obstructed it was with roots, or even how limited he was by the tools he was using. Yet his answer conveyed information to his questioner. It conveyed even more information, we suspect, to his boss, for it gave him a firm measure to use as a basis for estimating time and cost and time to completion.”

Originally, this technique could probably be used in the conditions mentioned by the Park. Later models such as COCOMO (Boehm 1981) also allowed developers take into account a number of parameters whose variability was probably reasonable at the time. But since then, the number of factors affecting the number of lines of code has become so important that it is very unwise to take this action seriously both in the evaluation of the software and the productivity of the design team.  I will try to illustrate the problem using eight arguments.

1. Different languages and different frameworks

Today hundreds of different languages exist (Wikipedia 2013).  For each of these languages there are several frameworks.  For the same functionality, there may be a very different number of lines of code produced depending on which technology is chosen.  In addition, modern architectures use different technologies, which further complicates calculations. Correction factors exist but they hardly seem defensible given the wide variety of types of applications that are being developed today.

2. Experience and competence of the developers

We must also take into account the experience of the developers involved as this may affect the calculation in many ways. A very competent developer often writes fewer lines of code than other less experienced developers because they will use design methods created for the sole purpose of reducing the number of lines and increasing readability and maintainability. In addition, they are more competent with the functionalities offered by tools (technology stack). Indeed, through ignorance of these, many programmers rewrite existing code, greatly increasing the number of lines of code.  In this regard, many experts in the area do not hesitate to speak of “lines of code spent” as opposed to “lines of code produced” (Dijkstra 1983).

3. The practice of refactoring

The fact that the same piece of code can change over time with the refactoring (reworking of code) can skew the results. This practice reworks the source code without adding functionality (Wikipedia 2013) and it is becoming more common because it can increase code quality and reduce technical debt. This can cause unexpected situations: if many developers practice this technique while the lines of code are being measured, the result could give the appearance of a reduction in output (fewer lines of code than in the previous measurement), while it is clear that the opposite occurs.

4. The practice of reuse and / or code generation

The reuse of existing code is very common and highly recommended in DRY (Do not Repeat Yourself). So many parts of the code can be retrieved from a previous project or copied from an open source project, library or another blog post. In addition, modern development tools can automatically generate code for the developer who works with various high level design tools.

5.  Tasks outside development

Activity in the development of software is not limited to writing code on a keyboard. In fact, many other tasks are needed to produce quality code. Here, a high variability can emerge according to the different methods used, the composition of the team or the documentation requirements.

6. The reliability of the measurement tool

A wide variety of measurement tools are available on the market. Given the lack of consensus on the method of counting the amount of lines of code in a source file, the outcome may be materially different depending on the tool used.  In addition, certain technical problems can arise when it comes to identifying what should actually be counted or not. For example, some software has difficulty differentiating comments from instructions when they are mixed (Danial 2013). The efficiency and quality of those source line counter is also very variable.

7. The (potential) manipulations

When a measure may have an impact on one or more person, we need to consider the possibility that some of them try to manipulate it to their advantage. Thus, if the productivity of a developer is measured based on the number of lines of code (or functions), they could very easily manipulate the source code to inflate the results. This problem is very common in companies that use KPIs to conduct assessments of their employees. One can also easily imagine a company trying to maximise the numbers if they know they will be evaluated based on this metric.

8. Time

Almost all the above elements are time sensitive. For example, the competence of a developer does change with practice (this includes the famous learning curve). More features of languages ​​and frameworks are also evolving to increase the productivity of the developers. The longer a project takes, the longer the measurement will be sensitive to this bias

Conclusion

In conclusion we can say that estimating the production effort or value of a program using this software metric is very risky. However, this technique is widely used. Some estimate experts such as Steve McConnell (2006) are very aware of the ineffectiveness of the method but still use it in the absence of anything better. Other methods based on “function point” (business functionality) have attempted to resolve some of the issues addressed above, but the values ​​remain highly correlated with the number of lines of code (Albrecht 1983).  For me, the information obtained by these metrics, and anything based on them, should never be considered as reliable and should be used with great caution in your decision making process.

Note: Some of the information in this text come from the fruit of the research I have done for LIEU (Liaison Entreprises-Universités) a network of valorisation units of Universities and colleges of the Wallonia-Brussels federation.

References

Albrecht, A. (1983). Software Function, Source Lines of Code, and Development Effort Estimation. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1703110&searchWithin%3Dp_Authors%3A.QT.Albrecht%2C+%2FA%2F.J..QT.%26searchWithin%3Dp_Author_Ids%3A37850740200

Boehm, B. W. (1981). Software Engineering Economics.  Englewood Cliffs, NJ. http://userfs.cec.wustl.edu/~cse528/Boehm-SE-Economics.pdf

Danial, A. (2013). CLOC Limitations. Retrieved the 2 august 2013 from http://cloc.sourceforge.net/#Limitations

Dijkstra, E. W. (1983). The fruit of misunderstanding. Retrieved the 2 august 2013 from http://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD854.html

List of programming languages. (2013, July 30). In Wikipedia, The Free Encyclopedia. Retrieved 12:48, August 2, 2013, from http://en.wikipedia.org/w/index.php?title=List_of_programming_languages&oldid=566431816

McConnell, S. (2006). Software Estimation : Demystifying the Black Art.Microsoft Press. http://www.amazon.com/Software-Estimation-Demystifying-Practices-Microsoft/dp/0735605351/

Park, R. E. (1992). Software Size Measurement : A Framework for Counting Source Statements. http://www.sei.cmu.edu/reports/92tr020.pdf

Réusinage de code. (2013, juillet 5). Wikipédia, l’encyclopédie libre. Retrieved the 12:04, august 2, 2013 from  http://fr.wikipedia.org/w/index.php?title=R%C3%A9usinage_de_code&oldid=94719037

The sprint review meeting is crucial for you, the developer, because this is the time you will be able to both give a lot of visibility to what you are doing and also get the feedback necessary to better align your work on the real needs of customer and or users. Yet too often, this meeting does not achieve these objectives because it has been badly prepared.

Show what you have done

I would like to draw your attention to two of the main problems that may be encountered.

Firstly, showing the result of work that has no user interface, and then secondly making sure you show something that works.

For the first point, the difficulty intensifies when you are dealing with people who have never been programmers. They often do not realize that a developer can sometimes work for weeks for minimal changes to the user interface. In other words, they can hear what you have to tell them, but they cannot see it. The solution developers choose far too often is just not talk about it.  If you can’t demonstrate it – don’t show it.

This attitude is typical of personalities we technicians have.  It is undesirable because you cannot then get the views of your user on the progress.  Their understanding and view is much more important than yours.  You are first and foremost in their service, and not the reverse. Developers who comprehend this have a very significant advantage over others.

The best way to show something that will not be obvious in the user interface is to use a presentation slide that describes the change. Because every time you develop software it is supposed to add value, you must find a way to highlight it. Some examples:

• For performance improvements, clearly indicate the gain in a metric understood by the user.
• If you have completed some intensive code refactoring, you should be able to demonstrate the usefulness of the work by using metrics highlighting the reduction of complexity, testability, or other value that will in the long term give significant savings in maintaining the code.
• For the functions for which it is impossible to show something, such as support for a new source of information in a communication tool, do not settle for spending a few moments going through an a list of work done. Describe the challenges you encountered – this should give them the context that they lack.

In any case you should avoid sounding as if you are just justifying the time you have spent on the work.  The idea here is to evidence that you have progressed and that you are in control of the situation.

One last thing about the demonstration: Make sure you do the following things:

• draft a scenario that you will repeat to your users.  You must not just randomly chat about the project
• This scenario should cover most of the needs expressed by the client. This makes sure you are not interrupted by constant questions during the demonstration. In addition, this will allow you to ensure good feedback (we will cover this again below).
• Run the demonstration several times on the machine that will be used during sprint review. This should eliminate most problems.
• Rehearse it once or twice under the same conditions as the meeting. That is to say physically in the room with the same equipment.

Collect feedback

Great developers excel in this task. They know that developers who give the most satisfaction to their users or customers are those who understand and realize the things that bring their customers the most value. This type of profile is very rare and it is again very difficult for a technician to fully grasp this concept.

Everyone knows that the users are usually more comfortable when describing their problems than with finding solutions. There must be a real interaction between the technician who will eventually devise the solution and the person who has the problem. For this interaction to be productive, the programmer should be able to ask the right questions and discuss areas for improvement – and this is especially true during sprint reviews.

Many developers fear this time because they are likely to be criticized. This particularly affects perfectionists. Here are some tips to manage any negative reviews:

• Be aware that criticism of your work is primarily criticism of the latter – the work, and it would be inappropriate to assume that the customer is actually criticizing you.
• Remember that criticism relates mainly to the person who has made it and how they have interpreted the situation.

Having considered the two points above, we must consider every criticism as an opportunity for improvement.

Of course, all this also applies to positive feedback and I will write an article soon that will cover this in detail.

To summarize:

• You can show your progress and all your activities
• Feedback is an opportunity for you to excel in your profession

You should always consider using existing software components instead of developing your own; even if you think that the latter would be much better. Here are 6 reasons why working with third party projects (open source or commercial) is usually a better choice:

– Domain expertise: Authors are usually experts in the domain covered by the library.  This will ensure that you will get the most appropriate implementation.  A good example is SharpMap.  The main committers are experts in geospacial software.

– Stability: These libraries have the big advantage of being used by other people as well as you, and in many cases, hundreds if not many thousands of developers worldwide.  Most of the early problems have already been encountered by others and fixed by authors.  If they don’t fix them, it’s a good opportunity for you to contribute and give back to the community!

– Knowledge: You will learn from others’ code and design. Many popular libraries are written by top notch developers and are usually great examples of good coding practices and design.  You will learn by just using them.

– Finance: You save tons of money.  The equivalent of hundreds if not thousands of man days of work for free or, at the very worst, the cost of one man day for commercial libraries.

– Support: Paid libraries usually come with free support from top class developers that you can contact 24h a day.  Many developers of free libraries also provide that level of support.  Exposing your team to these developers will be beneficial for them.

– New features: They will appear automatically, without effort, in your product.  If you are using the reporting engine from vendor X, and vendor X releases the new feature Y, you will be able to provide the new Y feature to your customer at no cost, with very low effort.  You can consider the authors of your libraries as other teams working for you, for free or for very little money!

So, assuming that you are not an expert in the domain, don’t have thousands of users, have lots to learn from others, don’t have tons of money, will probably need support and resources are stretched, don’t reinvent the wheel.  Unless you plan on learning more about the wheel.

Do you think NASA would have been able to send men to the moon if they tried to build the components of their rocket themselves?

First a few words about the idea of offshoring. Many developers think offshoring is a threat to their job. I can tell you right now that if you can potentially be replaced by an offshore developer, then yes, you have a big problem. You are just a developer. Being just a developer can get you replaced fairly quickly, even by another local developer. To avoid being replaced you must become a problem solver: a problem solver whose specialty is software development.

This post is not about the potential threat of offshoring for your job, so if you are particularly concerned by that, keep reading this blog, I have few articles directly related to developer job security coming.  Meanwhile, you can check existing content that covers the subject. A few years ago, Chad Fowler wrote a book called My Job Went to India: 52 Ways to Save Your Job, later renamed The Passionate Programmer: Creating a Remarkable Career in Software Development (Pragmatic Life). I highly suggest you purchase the book as it summarizes it pretty well.

The advantages of outsourcing

There are several advantages for local development teams in outsourcing a part of their software projects.

• You have access to talent worldwide: given the difficulty for western countries to find skills locally, access to talent worldwide is a real asset. There are tons of great developers out there waiting to provide you the best.
• Cost controlled to the cent: outsourced projects can be started and stopped very quickly.
• Highly scalable: because of the availability of the skills out there, it’s very easy to scale up.
• Empower your local team: if you use outsourcing to empower your local team instead of trying to replace it, you’ll get best results.
• You contribute to a better world: a developer you hire in India is able to support a whole family alone with only one salary. If you pay him the same rate as in your own country (which I do often when I’m financially able to), a whole village can be supported.

The only potential disadvantages are directly related to the way you manage them. Over the past 10 years I have outsourced up to 400 projects with hundreds of different providers spread over all 5 continents. Here are the 10 core principles that allowed me to reach almost 100% success rate with my recent outsourced projects. These 10 principles are divided into 3 categories: choice, supervision & strategy.

Choice

Choice is about picking the right guys. Choice is very important and you should spend time on it.