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10X Programmer and other Myths in Software Engineering – Interview with Laurent Bossavit

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We’ve interviewed Laurent Bossavit, a consultant and Director at Institut Agile in Paris, France. We discuss his book ’The Leprechauns of Software Engineering’, which debunks myths common in Software Engineering. He explains how folklore turns into fact and what to do about it. More specifically we hear about findings of his research into the primary sources of theories like the 10X Programmer, the Exponential Defect Cost Curve and the Software Crisis.

Content and Timings

  • Introduction (0:00)
  • About Laurent (0:22)
  • The 10X Programmer (1:52)
  • Exponential Defect Cost Curve (5:57)
  • Software Crisis (8:15)
  • Reaction to His Findings (11:05)
  • Why Myths Matter (14:44)

Transcript

Introduction

Derrick:
Laurent Bossavit is a consultant and Director at Institut Agile in Paris, France. An active member of the agile community he co-authored the first French book on Extreme Programming. He is also the author of “The Leprechauns of Software Engineering”. Laurent, thank you so much for joining us today, can you share a little bit about yourself.

About Laurent

Laurent:
I am a freelance consultant working in Paris, I have this great privilege. My background is as a developer. I try to learn a little from anything that I do, so after a bit over 20 years of that I’ve amassed a fair amount of insight, I think, I hope.

Derrick:
Your book, “The Leprechauns of Software Engineering”, questions many claims that are entrenched as facts and widely accepted in the software engineering profession, what made you want to write this book?

Laurent:
I didn’t wake up one morning and think to myself, ‘I’m going to write a debunkers book on software engineering’ but it actually was the other way around. I was looking for empirical evidence anything that could serve as proof for agile practices. And while I looked at this I was also looking at evidence for other things which are in some cases, were, related to agile practices for instance the economics of defects and just stuff that I was curious about like the 10X programmers thing. So, basically, because I was really immersed in the literature and I’ve always been kind of curious about things in general, I went looking for old articles, for primary sources, and basically all of a sudden I found myself writing a book.

The 10X Programmer

Derrick:
So, let’s dig into a few of the the examples of engineering folklore that you’ve examined, and tell us what you found. The first one you’ve already mentioned is the 10X programmer. So this is the notion that there is a 10 fold difference between productivity and quality of work between different programmers with the same amount of experience. Is this fact or fiction?

Laurent:
It’s actually one that I would love to be true if I could somehow become or if I should find myself as a 10X programmer. Maybe I would have an argument for selling myself for ten times the price of cheaper programmers. When I looked into it, what was advanced as evidence for those claims, what I found was not really what I had expected, what you think would be the case for something people say, and what you think is supported by tens of scientific studies and research into software engineering. In fact what I found when I actually investigated, all the citations that people give in support for that claim, was that in many cases the research was done on very small groups and not extremely representative, the research was old so this whole set of evidence was done in the seventies, on programs like Fortran or COBOL and in some cases on non-interactive programming, so systems where the program was input, you get results of the compiling the next day. The original study, the one cited as the first was actually one of those, it was designed initially not to investigate productivity differences but to investigate the difference between online and offline programming conditions.

So how much of that is still relevant today is debatable. How much we understand about the concept of productivity itself is also debatable. And also many of the papers and books that were pointed to were not properly scientific papers. They were opinion pieces or books like Peopleware, which I have a lot of respect for but it’s not exactly academic. The other thing was that some of these papers did not actually bring any original evidence in support of the notion that some programmers are 10X better than others, they were actually saying, “it is well known and supported by ‘this and that’ paper” and when I looked at that the original paper they were referencing, they were in turn saying rather than referencing their own evidence, saying things like “everybody knows since the seventies that some programmers are ten times more than others” and very often after chasing after all the references of old papers, you ended up back at the original paper. So a lot of the evidence was also double counted. So my conclusion was, and this was the original leprechaun, my conclusion was that the claim was not actually supported. I’m not actually coming out and saying that its false, because what would that mean? Some people have taken me to task for saying that all programmers are the same, and that’s obviously stupid, so I can not have been saying that. What I’ve been saying is that the data is not actually there, so we do not have any strong proof of the actual claim.

Exponential Defect Cost Curve

Derrick:
There is another folklore item called the “exponential defect cost curve”. This is the claim that it can cost one dollar to fix a bug during the requirements stage, then it will take ten times as much to fix in code, one hundred times in testing, one thousand times in production. Right or wrong?

Laurent:
That one is even more clear cut. When you look at the data and you try to find what exactly was measured, because those are actual dollars and cents, right? So it should be the case, at some point a ledger or some kind of accounting document originates the claim. So I went looking for the books that people pointed me to and typically found that rather than saying we did the measurements from this or that project, books said or the articles said, ‘this is something everybody knows’, and references were ‘this or that article or book’. So I kept digging, basically always following the pointers back to what I thought was the primary source. And in many cases I was really astonished to find that at some point along the chain basically someone just made evidence up. I could not find any solid proof that someone had measured something and came up with those fantastic costs, sometimes come across like, fourteen hundred and three dollars on average per bug, but what does that even mean? Is that nineteen-nineties dollars? These claims have been repeated exactly using the same numbers for I think at least three decades now. You can find some empirical data in Barry Boehm’s books and he’s often cited as the originator of the claim. But it’s much less convincing when you look at the original data than when you look at the derived citations.

The Software Crisis

Derrick:
There is a third folklore, called “The Software Crisis”. Common in mainstream media reporting of large IT projects. These are studies that highlight high failure rates in software projects, suggesting that all such projects are doomed to fail. Are they wrong?

Laurent:
This is a softer claim right, so there’s no hard figures, although some people try. So, one of the ways one sees software crises exemplified is by someone claiming that software bugs cost the U.S. economy so many billions, hundreds of billions of dollars per year. A more subjective aspect of the notion of the software crisis, historically what’s interesting, is the very notion of the software crisis was introduced to justify the creation of a group for researching software engineering. So the initial act was the convening of the conference on software engineering, that’s when the term was actually coined and that was back in 1968, and one of the tropes if you will, to justify the interest in the discipline was the existence of the software crisis. But today we’ve been basically living with this for over forty years and things are not going so bad, right? When you show people a dancing bear one wonders not if the bear dances well. That it dances at all. And to me technology is like that. It makes amazing things possible, it doesn’t always do them very well but its amazing that it does them at all. So anyway I think the crisis is very much over exploited, very overblown, but where I really start getting into my own, getting on firmer ground is when people try to attach numbers to that. And typically those are things like a study that supposedly found that bugs were costing the U.S. sixty billion dollars per year and when you actually take a scalpel to the study, when you read it very closely and try to understand what methodology they were following and exactly how they went about their calculations, what you found out is that they basically picked up the phone and interviewed over the phone a very small sample of developers and asked them for their opinion, which is not credible at all.

Reaction to His Findings

Derrick:
What is a typical reaction to your findings debunking these long held claims?

Laurent:
Well, somewhat cynically it varies between, “Why does that matter?” and a kind of violent denial. Oddly enough I haven’t quite figured out why, what makes people so into one view point or the other and there’s a small but substantial faction of people who tell me ‘oh that’s an eye opener’ and would like to know more, but some people respond with protectiveness when they see for instance the 10X claim being attacked. I’m not quite sure I understand that.

Derrick:
So how do myths like these come about?

Laurent:
In some cases you can actually witness the birth of a leprechaun. Its kind of exciting. So, some of them come about from misunderstandings. I found out in one case for instance that an industry speaker gave a talk at a conference and apparently he was misunderstood. So people repeated what they thought they had heard and one thing led to another. After some iterations of this telephone game, a few people, including some people that I know personally, were claiming in the nineties that the waterfall methodology was causing 75% failure rates in defence projects, and it was all a misunderstanding when I went back and looked at the original sources the speaker was actually referring to a paper from the seventies which was about a sample of nine projects. So not an industry wide study. So I think that was an honest mistake, it just snow-balled. In some cases people are just making things up, so that’s one way to convince people, just make something up. And one problem is that it takes a lot more energy to debunk a claim than it takes to just make things up. So if enough people play that little game some of that stuff is going to just sneak past. I think the software profession kind of amplify the problem by offering fertile ground, we tend to be very fashion driven, so we enthusiastically jump onto bandwagons. That makes it easy for some people to invite others to jump, to propagate. So I think we should be more critical, there has been a movement towards evidence-based software engineering which I think is in some ways misguided, but is good news to my way of thinking that anyone is able to think, maybe we shouldn’t be so gullible.

Why Myths Matter

Derrick:
Even if the claims are in fact wrong, why does it matter?

Laurent:
To me, one of the key activities in what we do, its not typing code, its not design, its not even listening to customers, although, that comes close. The key activity that we perform is learning. We are very much a learning-centered profession so to speak. Because the very act of programming when looked at from a certain perspective is just about capturing knowledge of the world about businesses, about… other stuff that exists out there in the real world or is written about that is already virtual, but in any way capturing knowledge and encoding that knowledge, capturing it in code, in executable forms, so learning is one of the primary things that we do anyway already. I don’t think we can be good at that if we are bad at learning in general in a more usual sense. But what happens to those claims which are easy to remember, easy to trot out, they act as curiosity stoppers, basically. So they prevent us from learning further and trying to get at the reality, at what actually goes on in the software development project, what determines whether a software project is a success or failure, and I think that we should actually find answers to these questions. So it is possible to know more than we do right now, I am excited every time that I learn something that makes more sense for me that helps me have a better grip on developing software.

Derrick:
Are there any tell-tale signs that we can look out for to help stop ourselves from accepting such myths?

Laurent:
Numbers, statistics, citations, strangely. I know that citations are a staple inside of academic and scientific writing but when you find someone inside the software engineering profession that is whipping out citations at the drop of a hat, you should take that as a warning sign. And there is more but you know we would have to devote an hour or so to that.

Derrick:
Thank you so much for joining us today, it has been great.

Laurent:
Thanks for having me, bye!