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Here's an idea. There are a number of open-source software projects that exist. Many of these are in some sort of version control system which, generally, keeps a number of important records; any change made to the software will include a timestamp, a note by the programmer detailing what was the intention of the change, and a list of changes to the files that resulted from the change.
A simple experiment might then be to simply collate data from either one large project, or a number of smaller projects. The cost of fixing a bug can be estimated from the number of lines of code changed to fix the bug; the amount of time since the bug was introduced can be found by looking back through previous versions and comparing timestamps. A scatter plot of time vs. lines-of-code-changed can then be produced, and investigated for trends.
Of course, this would require a fair investment of time to do it properly.
And time is money, so that doesn't really fit the "cheap and easy" constraint I specified.
Hmmm. I'd parsed 'cheap and easy' as 'can be done by a university student, on a university student's budget, in furtherance of a degree' - which possibly undervalues time somewhat.
At the cost of some amount of accuracy, however, a less time-consuming method might be the following; to automate the query, under the assumption that the bug being repaired was introduced at the earliest time when one of the lines of code modified to fix the bug was last modified (that is, if three lines of code were changed to fix the bug, two of which had last been changed on 24 June and one of which had last been changed on 22 June, then the bug would be assumed to have been introduced on 22 June). Without human inspection of each result, some extra noise will be introduced into the final graph. (A human (or suitable AGI, if you have one available) inspection of a small subset of the results could give an estimate of the noise introduced)
By "cheap and easy" what I mean is "do the very hard work of reasoning out how the world would behave if the hypothesis were true, versus if it were false, and locate the smallest observation that discriminates between these two logically possible worlds".
That's hard and time-consuming work (therefore expensive), but the experiment itself is cheap and easy.
My intuition (and I could well be Wrong on this) tells me that experiments of the sort you are proposing are sort of the opposite: cheap in the front and expensive in the back. What I'm after is a mullet of an experiment, business in front and party in back.
An exploratory experiment might consist of taking notes the next time you yourself fix a bug, and note the answers to a bunch of hard questions: how did I measure the "cost" of this fix? How did I ascertain that this was in fact a "bug" (vs. some other kind of change)? How did I figure out when the bug was introduced? What else was going on at the same time that might make the measurements invalid?
Asking these questions, ISTM, is the real work of experimental design to be done here.
Well, for a recent bug; first, some background:
Then, to answer your questions in order:
Once the problem code was identified, the fix was done in a few minutes. Identifying the problem code took a little longer, as the problem was a rare and sporadic one - it happened first during a particularly irritating test case (and then, entirely by coincidence, a second time on a similar test case, which caused some searching in the wrong bit of code at first)
A numeric value, displayed to the user, was showing "NaN".
The bug was introduced by failing to consider a rare but theoretically possible test case at the time (near the beginning of a long project) that a certain utility function was produced. I could get a time estimate by checking version control to see when the function in question had been first written; but it was some time ago.
A more recent change made the bug slightly more likely to crop up (by increasing the potential for rounding errors). The bug may otherwise have gone unnoticed for some time.
Of course, that example may well be an outlier.
Hmmm. Thinking about this further, I can imagine whole rafts of changes to the specifications which can be made just before final testing at very little cost (e.g. "Can you swap the positions of those two buttons?") Depending on the software development methodology, I can even imagine pretty severe errors creeping into the code early on that are trivial to fix later, once properly identified.
The only circumstances I can think of that might change how long a bug takes to fix as a function of how long the development has run are:
Good stuff! One crucial nitpick:
That doesn't tell me why it's a bug. How is 'bug-ness' measured? What's the "objective" procedure to determine whether a change is a bug fix, vs something else (dev gold-plating, change request, optimization, etc)?
NaN is an error code. The display was supposed to show the answer to an arithmetical computation; NaN ("Not a Number") means that, at some point in the calculation, an invalid operation was performed (division by zero, arccos of a number greater than 1, or similar).
It is a bug because it does not answer the question that the arithmetical computation was supposed to solve. It merely indicates that, at some point in the code, the computer was told to perform an operation that does not have a defined answer.
That strikes me as a highly specific description of the "bug predicate" - I can see how it applies in this instance, but if you have 1000 bugs to classify, of which this is one, you'll have to write 999 more predicates at this level. It seems to me, too, that we've only moved the question one step back - to why you deem an operation or a displayed result "invalid". (The calculator applet on my computer lets me compute 1/0 giving back the result "ERROR", but since that's been the behavior over several OS versions, I suspect it's not considered a "bug".)
Is there a more abstract way of framing the predicate "this behavior is a bug"? (What is "bug" even a property of?)
Ah, I see - you're looking for a general rule, not a specific reason.
In that case, the general rule under which this bug falls is the following:
For any valid input, the software should not produce an error message. For any invalid input, the software should unambiguously display a clear error message.
'Valid input' is defined as any input for which there is a sensible, correct output value.
So, for example, in a calculator application, 1/0 is not valid input because division by zero is undefined. Thus, "ERROR" (or some variant thereof) is a reasonable output. 1/0.2, on the other hand, is a valid operation, with a correct output value of 5. Returning "ERROR" in that case would be a bug.
Or, to put it another way; error messages should always have a clear external cause (up to and including hardware failure). It should be obvious that the external cause is a case of using the software incorrectly. An error should never start within the software, but should always be detected by the software and (where possible) unambiguously communicated to the user.
Granting that this definition of what constitutes a "bug" is diagnostic in the case we've been looking at (I'm not quite convinced, but let's move on), will it suffice for the 999 other cases? Roughly how many general rules are we going to need to sort 1000 typical bugs?
Can we even tell, in the case we've been discussing, that the above definition applies, just by looking at the source code or revision history of the source code? Or do we need to have a conversation with the developers and possibly other stakeholders for every bug?
(I did warn up front that I consider the task of even asking the question properly to be very hard, so I'll make no apologies for the decidedly Socratic turn of this thread.)