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Wanted: 99 effort estimation datasets
Every now and again, I stumble upon a really interesting dataset. Previously, when this happened I wrote an extensive blog post; but the SiP dataset was just too big and too detailed, it called out for a more expansive treatment.
How big is the SiP effort estimation dataset? It contains 10,100 unique task estimates, from ten years of commercial development using Agile. That’s around two orders of magnitude larger than other, current, public effort datasets.
How detailed is the SiP effort estimation dataset? It contains the (anonymized) identity of the 22 developers making the estimates, for one of 20 project codes, dates, plus various associated items. Other effort estimation datasets usually just contain values for estimated effort and actual effort.
Data analysis is a conversation between the person doing the analysis and the person(s) with knowledge of the application domain from which the data came. The aim is to discover information that is of practical use to the people working in the application domain.
I suggested to Stephen Cullum (the person I got talking to at a workshop, a director of Software in Partnership Ltd, and supplier of data) that we write a paper having the form of a conversation about the data; he bravely agreed.
The result is now available: A conversation around the analysis of the SiP effort estimation dataset.
What next?
I’m looking forward to seeing what other people do with the SiP dataset. There are surely other patterns waiting to be discovered, and what about building a simulation model based on the charcteristics of this data?
Turning software engineering into an evidence-based disciple requires a lot more data; I plan to go looking for more large datasets.
Software engineering researchers are a remarkable unambitious bunch of people. The SiP dataset should be viewed as the first of 100 such datasets. With 100 datasets we can start to draw general, believable conclusions about the processes involved in software effort estimation.
Readers, today is the day you start asking managers to make any software engineering data they have publicly available. Yes, it can be anonymized (I am willing to do that for people who are looking to release data). Yes, ‘old’ data is useful (data from the 1980s could have an interesting story to tell; SiP runs from 2004-2014). Yes, I will analyze any interesting data that is made public for free.
Ask, and you shall receive.
Maximizing profit selling C compilers
Upgrades are the lifeblood of established software companies. I recently came across the paper Information Goods Upgrades: Theory and Evidence and what caught my attention was one of the datasets the author had collected, first purchase and upgrade price of various PC C/C++ compilers between 1987 and 1997. What’s more the author still had the data and was willing to share it, yay!
By the early 1990s I was no longer actively involved in C compilers, but was involved in C static analysis on non-PC platforms. So my view of the 1990s C compiler market is a bit sketchy.
Compiler companies, like other companies, want to maximize their revenue and THE decision that has to be made is the price to charge for a compiler (compiler writers are also developers and hate high prices for compilers and those that failed to charge enough for their product soon went bust). My recollection is that compiler pricing was based around the spending authority of a senior development engineer and also what other companies were charging. Just under £500 was common, with a few companies failing to make a go of selling around the £100 mark. Zorland (later renamed to Zortech) gained huge market share in the mid/late 1980s selling a great C compiler for £29, but a few years later were selling a C++ compiler for a lot more.
To some extent each compiler vendor operates in a monopoly market; developers write code that depends on the features supported by the compiler used and it can be very expensive to port code to a different compiler. How much can vendors charge for a compiler upgrade? Selling the product at a high price provides a rationale for higher priced upgrades (the percentage discount will look good). I wonder how many vendors continued to advertise a high price product just to justify a high upgrade price.
Management always feel an affinity for the OS vendor and Microsoft sold a C compiler and later a C++ compiler. They were both awful and easy, product quality wise, to compete against. Microsoft had to have their own compiler for strategic internal use, with sales to developers being insignificant compared to sales of Word and Excel (Microsoft compiler people I talked to at the time said they had thought of giving the compiler away for free and later it was possible to essentially get the compiler for free by joining the various developer programs). Over time Microsoft improved and compiler companies found easier ways to make money, so the number of compiler vendors dropped to almost one (a company selling C compiler validation suites once told me in the late 1990s that they had sold over 150 copies; someone has to be serious about their compiler to shell out $5,000-$10,000 for software to test it).
By the late 1980s the C compiler market was quite saturated and vendors needed something else to sell. IDEs and debuggers were popular choices. Then along came C++. Yay! A new language meant a new compiler to sell. Compiler vendors’ need for a new compiler to sell is a significantly underestimated factor in C++ gaining traction in developer mind share.
A rarely talked about compiler revenue stream is being paid to port a compiler to a new platform (either because there is an important application hat depend son it or because the platform does not yet have a C compiler). This is the market where gcc had its first successes. Its hard to say whether gcc spread because these niche platforms spread or because gcc cut off revenue to compiler vendors making remaining in the compiler market unattractive to them.
I don’t have any sales figures for any ‘mass’ market C compilers or compilers for any languages. Can any readers help out? In fact any data on compiler sales would be most welcome.
Memory capacity and commercial compiler development
When I started out in the compiler business in the 80s many commercial compilers were originally written by one person. A very good person who dedicated himself (I have never heard of a woman doing this) to the job (i.e., minimal social life) could produce a commercially viable product for a non-huge language (e.g., Fortran, Pascal, C, etc and not C++, Ada, etc) in 12-18 months. Companies who decide to develop a compiler in-house tend to use a team of people and take longer because that is how they work, and they don’t want to depend on one person and anyway such a person might not be available to them.
Commercially viable compiler development stayed within the realm of an individual effort until sometime in the early 90s. What put the single individual out of business was the growth in computer memory capacity into the hundreds of megabytes and beyond. Compilers have to be able to run within the limits of developer machines; you won’t sell many compilers that require 100M of memory if most of your customers don’t have machines with 100M+ of memory.
Code optimizers eat memory and this prevented many optimizations that had been known about for years appearing in commercial products. Once the machines used for software development commonly contained 100M+ of memory compiler vendors could start to incorporate these optimizations into their products.
Improvements in processor speed also helped. But developers are usually willing to let the compiler take a long time to optimize the code going into a final build, provided development compiles run at a reasonable speed.
The increase in memory capacity created the opportunity for compilers to improve and when some did improve they made it harder for others to compete. Suddenly an individual had to spend that 12-18 months, plus many more months implementing fancy optimizations; developing a commercially viable compiler outgrew the realm of individual effort.
Some people claim that the open source model was the primary driver in killing off commercial C compiler development. I disagree. My experience of licensing compiler source was that companies preferred a commercial model they were familiar with and reacted strongly against the idea of having to make available any changes they made to the code of an open source program. GCC (and recently llvm) succeeded because many talented people contributed fancy optimizations and these could be incorporated because developer machines contained lots of memory. If storage had not dramatically increased gcc would probably not be the market leader it is today.
Criteria for knowing a language
What does it mean for somebody to claim to know a computer language? In the commercial world it means the person is claiming to be capable of fluently (i.e., only using knowledge contained in their head and without having to unduly ponder) reading, and writing code in some generally accepted style applicable to that language. The academic world generally sets a much lower standard of competence (perhaps because most of its inhabitants leave before any significant expertise is acquired). If I had a penny for every recent graduate who claimed to know a language and was incapable of writing a program that read in a list of integers and printed their sum (I know companies that set tougher problems, but they do not seem to have higher failure rates), I would be a rich man.
One experiment asked 21 postgraduate and academic staff which of the following individuals they would regard as knowing Java:
The results were:
_ NO YES
A 21 0
B 18 3
C 16 5
D 8 13
E 0 21
These answers reflect the environment from which the subjects were drawn. When I wrote compilers for a living, I did not consider that anybody knew a language unless they had written a compiler for it, a point of view echoed by other compiler writers I knew.
I’m not sure that commercial developers would be happy with answer (E), in fact they could probably expand (E) into five separate questions that tested the degree to which a person was able to combine various elements of the language to create a meaningful whole. In the commercial world, stage (E) is where people are expected to start.
The criteria used to decide whether somebody knows a language depends on which group of people you talk to; academics, professional developers and compiler writers each have their own in-group standards. In a sense the question is irrelevant, a small amount of language knowledge applied well can be used to do a reasonable job of creating a program for most applications.
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