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Likelihood of a fault experience when using the Horizon IT system
It looks like the UK Post Office’s Horizon IT system is going to have a significant impact on the prosecution of cases that revolve around the reliability of software systems, at least in the UK. I have discussed the evidence illustrating the fallacy of the belief that “most computer error is either immediately detectable or results from error in the data entered into the machine.” This post discusses what can be learned about the reliability of a program after a fault experience has occurred, or alleged to have occurred in the Horizon legal proceedings.
Sub-postmasters used the Horizon IT system to handle their accounts with the Post Office. In some cases money that sub-postmasters claimed to have transferred did not appear in the Post Office account. The sub-postmasters claimed this was caused by incorrect behavior of the Horizon system, the Post Office claimed it was due to false accounting and prosecuted or fired people and sometimes sued for the ‘missing’ money (which could be in the tens of thousands of pounds); some sub-postmasters received jail time. In 2019 a class action brought by 550 sub-postmasters was settled by the Post Office, and the presiding judge has passed a file to the Director of Public Prosecutions; the Post Office may be charged with instituting and pursuing malicious prosecutions. The courts are working their way through reviewing the cases of the sub-postmasters charged.
How did the Post Office lawyers calculate the likelihood that the missing money was the result of a ‘software bug’?
Horizon trial transcript, day 1, Mr De Garr Robinson acting for the Post Office: “Over the period 2000 to 2018 the Post Office has had on average 13,650 branches. That means that over that period it has had more than 3 million sets of monthly branch accounts. It is nearly 3.1 million but let’s call it 3 million and let’s ignore the fact for the first few years branch accounts were weekly. That doesn’t matter for the purposes of this analysis. Against that background let’s take a substantial bug like the Suspense Account bug which affected 16 branches and had a mean financial impact per branch of £1,000. The chances of that bug affecting any branch is tiny. It is 16 in 3 million, or 1 in 190,000-odd.”
That 3.1 million comes from the calculation: 19-year period times 12 months per year times 13,650 branches.
If we are told that 16 events occurred, and that there are 13,650 branches and 3.1 million transactions, then the likelihood of a particular transaction being involved in one of these events is 1 in 194,512.5. If all branches have the same number of transactions, the likelihood of a particular branch being involved in one of these 16 events is 1 in 853 (13650/16 -> 853); the branch likelihood will be proportional to the number of transactions it performs (ignoring correlation between transactions).
This analysis does not tell us anything about the likelihood that 16 events will occur, and it does not tell us anything about whether these events are the result of a coding mistake or fraud.
We don’t know how many of the known 16 events are due to mistakes in the code and how many are due to fraud. Let’s ask: What is the likelihood of one fault experience occurring in a software system that processes a total of 3.1 million transactions (the number of branches is not really relevant)?
The reply to this question is that it is not possible to calculate an answer, because all the required information is not specified.
A software system is likely to contain some number of coding mistakes, and given the appropriate input, any of these mistakes may produce a fault experience. The information needed to calculate the likelihood of one fault experience occurring is:
- the number of coding mistakes present in the software system,
- for each coding mistake, the probability that an input drawn from the distribution of input values produced by users of the software will produce a fault experience.
Outside of research projects, I don’t know of any anyone who has obtained the information needed to perform this calculation.
The Technical Appendix to Judgment (No.6) “Horizon Issues” states that there were 112 potential occurrences of the Dalmellington issue (paragraph 169), but does not list the number of transactions processed between these ‘issues’ (which would enable a likelihood to be estimated for that one coding mistake).
The analysis of the Post Office expert, Dr Worden, is incorrect in a complicated way (paragraphs 631 through 635). To ‘prove’ that the missing money was very unlikely to be the result of a ‘software bug’, Dr Worden makes a calculation that he claims is the likelihood of a particular branch experiencing a ‘bug’ (he makes the mistake of using the number of known events, not the number of unknown possible events). He overlooks the fact that while the likelihood of a particular branch experiencing an event may be small, the likelihood of any one of the branches experiencing an event is 13,630 times higher. Dr Worden’s creates complication by calculating the number of ‘bugs’ that would have to exist for there to be a 1 in 10 chance of a particular branch experiencing an event (his answer is 50,000), and then points out that 50,000 is such a large number it could not be true.
As an analogy, let’s consider the UK National Lottery, where the chance of winning the Thunderball jackpot is roughly 1 in 8-million per ticket purchased. Let’s say that I bought a ticket and won this week’s jackpot. Using Dr Worden’s argument, the lottery could claim that my chance of winning was so low (1 in 8-million) that I must have created a counterfeit ticket; they could even say that because I did not buy 0.8 million tickets, I did not have a reasonable chance of winning, i.e., a 1 in 10 chance. My chance of winning from one ticket is the same as everybody else who buys one ticket, i.e., 1 in 8-million. If millions of tickets are bought, it is very likely that one of them will win each week. If only, say, 13,650 tickets are bought each week, the likelihood of anybody winning in any week is very low, but eventually somebody will win (perhaps after many years).
The difference between the likelihood of winning the Thunderball jackpot and the likelihood of a Horizon fault experience is that we have enough information to calculate one, but not the other.
The analysis by the defence team produced different numbers, i.e., did not conclude that there was not enough information to perform the calculation.
Is there any way that the information needed to calculate the likelihood of a fault experience occurring?
In theory fuzz testing could be used. In practice this is probably completely impractical. Horizon is a data driven system, and so a copy of the database would need to be used, along with a copy of all the Horizon software. Where is the computer needed to run this software+database? Yes, use of the Post Office computer system would be needed, along with all the necessary passwords.
Perhaps, if we wait long enough, a judge will require that one party make all the software+database+computer+passwords available to the other party.
McCabe’s cyclomatic complexity and accounting fraud
The paper in which McCabe proposed what has become known as McCabe’s cyclomatic complexity did not contain any references to source code measurements, it was a pure ego and bluster paper.
Fast forward 10 years and cyclomatic complexity, complexity metric, McCabe’s complexity…permutations of the three words+metrics… has become one of the two major magical omens of code quality/safety/reliability (Halstead’s is the other).
It’s not hard to show that McCabe’s complexity is a rather weak measure of program complexity (it’s about as useful as counting lines of code).
Just as it is possible to reduce the number of lines of code in a function (by putting all the code on one line), it’s possible to restructure existing code to reduce the value of McCabe’s complexity (which is measured for individual functions).
The value of McCabe’s complexity for the following function is 5 16, i.e., and there are 16 possible paths through the function:
int main(void) { if (W) a(); else b(); if (X) c(); else d(); if (Y) e(); else f(); if (Z) g(); else h(); } |
each if…else contains two paths and there are four in series, giving 
paths.
Restructuring the code, as below, removes the multiplication of paths caused by the sequence of if…else:
void a_b(void) {if (W) a(); else b();} void c_d(void) {if (X) c(); else d();} void e_f(void) {if (Y) e(); else f();} void g_h(void) {if (Z) g(); else h();} int main(void) { a_b(); c_d(); e_f(); g_h(); } |
reducing main‘s McCabe complexity to 1 and the four new functions each have a McCabe complexity of two.
Where has the ‘missing’ complexity gone? It now ‘exists’ in the relationship between the functions, a relationship that is not included in the McCabe complexity calculation.
The number of paths that can be traversed, by a call to main, has not changed (but the McCabe method for counting them now produces a different answer)
Various recommended practice documents suggest McCabe’s complexity as one of the metrics to consider (but don’t suggest any upper limit), while others go as far as to claim that it’s bad practice for functions to have a McCabe’s complexity above some value (e.g., 10) or that “Cyclomatic complexity may be considered a broad measure of soundness and confidence for a program“.
Consultants in the code quality/safety/security business need something to complain about, that is not too hard or expensive for the client to fix.
If a consultant suggested that you reduced the number of lines in a function by joining existing lines, to bring the count under some recommended limit, would you take them seriously?
What about, if a consultant highlighted a function that had an allegedly high McCabe’s complexity? Should what they say be taken seriously, or are they essentially encouraging developers to commit the software equivalent of accounting fraud?
Software engineering: A great discipline for an academic fraudster
I am a sporadic reader of In the Pipeline, a blog covering drug discovery and the pharma industry, subjects about which I have no real interest but the author is a no nonsense guy whose writing I enjoy reading. A topic that regularly crops up is retraction of a published paper (i.e., effectively saying “ignore that paper we published way back when”). Reasons for retraction include a serious mistake, plagiarism of somebody else’s work or outright fabrication of data.
Retraction of papers published in software engineering journals is rare, why is that? I don’t think software engineering researchers are more/less honest than researchers in other fields. I could not find any entries on Retraction Watch.
Plagiarism certainly occurs and every now and again a paper is retracted for this reason.
Corrections to previously published papers certainly occur on a regular basis, but I don’t recall seeing a retraction because of a serious error (but then I rarely get to gossip around the coffee table in university departments and am not that well up on such goings on).
Researchers are certainly not above using the subset of a benchmark that shines the most favorable light on their work, or simply performing misleading comparisons. Researchers who do such things are seem more as an embarrassment than a threat to academic integrity, they are certainly not in the same league as those who fabricate data
Fabrication of data in software engineering? I’m sure it goes on, but unless the people responsible own up I think it is unlikely to be detected (unless the claims are truely over the top). There is no culture of replication in software engineering or of building on other peoples’ work (everybody is into doing their own thing); two very serious problems, but not the topic of this discussion.
In fact software engineering is the ideal discipline for an academic fraudster: replication is very rare, everyone doing their own thing, a culture of poor/nonexistent record keeping and experimental data is rarely kept past the replacement of the machine on which it sits (I am regularly told this when I email authors asking for a copy of their raw data for my book). Even in disciplines whose characteristics are at the other end of the culture scale, it can take a long time for fraud to be uncovered.
From time to time authors I contact tell me that the numbers appearing in the published paper are incorrect; often there is an offer of the correct numbers and sometimes a vague recollection of what they might be. Sometimes authors don’t reply to my email, is the data fake or is talking to me not worth their time (I have received replies to this effect)?
Am I worried about fraud in software engineering research? No, incorrect data in published work is more likely to occur because of clerical mistakes, laziness or incompetence.
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