[SystemSafety] Qualifying SW as "proven in use" [Measuring Software]

[James Ronback]

For estimating hardware reliability, engineers often use a parts count
and a reliability index for each part based on testing results or field
experience. We do not have a useful equivalent for software. Neither
lines of code nor the McCabe complexity index serve as a useful
surrogate for estimating the number of latent defects or the quality of
the code. We need to know what kind of exposure to operational inputs
the software has had in testing and in the field to estimate the number
of latent detects remaining.

The simplistic McCabe cyclomatic complexity metric is better used only
as a very rough estimate of the amount testing required to reveal
defects, rather than the amount of latent defects within a module, if
the module has no loops.

An alternate complexity measure that more closely resembles a parts
count is to enumerate the number of paths from entry to exit, counting
each elementary path and paths with loops with zero and at least two
traverses of each loop and paths with possible pairs of loops. The size
of this zero-two subset of a D-D graph would be a better measure of the
complexity of a module.

In a regular expression form of a D-D graph, you would replace loop
terms like (x )* with (x^2 + theta) which indicates repetition of 0 or
2 times, theta being a zero traversal.To determine the number of paths
to be tested, ZT, in the zero-two subset, you replace each variable in
the modified regular expression by 1 and evaluate the expression
arithmetically. This number is larger and more sensitive to complexity
and the related test effort than the metric provided by McCabe which
ignores the existence of loops since it is based on undirected graphs.
Test coverage of this resultant list of ZT paths would more likely
detect data flow anomalies (1), e.g., variables not being initialized
before use or updated twice without an intermediate use.

The size of ZT in terms of elementary loops and paths
= (eLoops^2 +1) * ePaths. An ePath is a path in which no loop is
traversed.

An upper bound, ZTmax on ZT, is the product of the indegrees of all the
decision nodes i in the D-D graph. The indegree of a decision node is
the number of branches entering a decision node from immediate
predecessor decision nodes.

The McCabe complexity index of 10 is often suggested as maximum for a
module under test.
A ZTmax value greater than 100 for a module may indicate it should be a
candidate for further decomposition/ redesign into smaller and more
testable and potentially more reusable modules.

As design defects are found and eliminated the software becomes more
reliable.

Assuming that a random test of each path in a Zero -Two subset of paths
is equi-probable, then the average number of random tries, T, to detect
and correct x faulty paths out of N paths is approximately:
T = N/x + N/(x-1) + N/(x-2) + ... + N/x + N
= N * 1/x + 1/(x-1) + 1/(x-2) ... + 1/2 + 1) a harmonic series
= N * ((log x) + 0.57 + 1/2x - 1/(12x^2) + 1/(120 x^4 - ...)
= N * ((log x) + 0.57) approximately

Thus It is the test effort that is severely underestimated resulting in
buggy software.

Yours safely,

Jim Ronback
in sunny Tsawwassen, BC

1) Detection of Data Flow Anomaly Through Program Instrumentation
Huang, J.C. ; Department of Computer Science, University of Houston
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1702622

On 2013-06-25 5:54 PM, Derek M Jones wrote:
> Steve,
>
>> I think we both strongly agree that there really needs to be a lot more
>> evidence.
> Yes.  No point quibbling over how little little might be.
>
>> But I'm not looking for a correlation of overall, total code base
>> cyclomatic complexity to overall defects. I'm looking for the correlation
>> of cyclomatic complexity within a single function/method to the defect
>> density within that same single function/method.
> Left to their own devices developers follow fairly regular patterns
> of code usage.  An extreme outlier of any metric is suspicious and
> often worth some investigation; it might be the case that
> the developer had a bad day or perhaps that function has to implement
> some complicated application functionality. or something else.
>
> Outliers are the low hanging fruit.
>
> The problems start, or rather the time wasting starts, when
> specific numbers get written into documents and is used to
> judge what developers produce.
>
>> along, what we need in the end is a balancing of a collection of syntactic
>> complexity metrics. When functions/methods are split, it always increases
>> fan out. When functions/methods are merged, it always decreases fan out.
>> The complexity didn't go away, it just moved to a different place in the
>> code. So having a limit in only one place easily allows people to squeeze
>> it into any other place. Having a set of appropriate limits means there's
>> a lot less chance of it going unnoticed somewhere else.
> Yes, what we need to lots of good quality data for lots of code
> attributes so we can start looking at these trade-offs.
> Unfortunately the only good quality data I have involves small
> numbers of attributes.
>
> Having seen what a hash some researchers make of analysing the data
> they have I am loath to accept finding where the data is not made
> available.
>
>> accident. Just the same, I'm basically arguing for more professionalism in
>> the software industry. I mean seriously, the programmer who was
>> responsible for that single C++ class with a single method of 3400 lines
>> of code with a cyclomatic complexity over 2400 is a total freaking moron
>> who has no business whatsoever in the software industry.
> We are not going to move towards professionalism until there are less
> software development jobs than half competent developers.  Hiring
> people based on their ability to spell 'software' is not an
> environment where professionalism takes root.
>
> I keep telling people that the best way to reduce faults in code is
> to start sending developers to prison.  Nobody take me seriously (ok,
> yes, it would probably be a difficult case to bring).
>
>> And, we will also always need semantic evaluation of code (which, as I
>> said earlier, has to be done by humans) because syntax-based metrics alone
>> will probably always be game-able.
> Until strong AI arrives that will not happen.
> Even the simpler issue of identifier semantics is still way beyond our
> reach.  See:
> http://www.coding-guidelines.com/cbook/sent792.pdf
> for more than you could ever want to know about identifier selection
> issues.
>
>> Regards,
>>
>> -- steve
>>
>>
>>
>>
>> -----Original Message-----
>> From: Derek M Jones <derek at knosof.co.uk>
>> Organization: Knowledge Software, Ltd
>> Date: Tuesday, June 25, 2013 4:21 PM
>> To: "systemsafety at techfak.uni-bielefeld.de"
>> <systemsafety at techfak.uni-bielefeld.de>
>> Subject: Re: [SystemSafety] Qualifying SW as "proven in use"
>> [Measuring	Software]
>>
>> Steve,
>>
>> ...
>>> "local vs. global" categories, it's just that nobody has yet published
>>> any
>>> data identifying which ones should be paid attention to and which ones
>>> should be ignored.
>> So you agree that there is no empirical evidence.
>>
>> Your statement is also true of almost every metrics paper published
>> todate.
>>
>> With so many different metrics having been proposed at least one of
>> them is likely to agree with the empirical data that is yet to be
>> published.
>>
>> You cited the paper: “A Practical Guide to Object-Oriented Metrics”
>> as the source of the cyclomatic complexity vs fault correlation
>> claim.  Fig 4 looks like it contains the data.  No standard
>> deviation is given for the values, but this would have to be
>> very large to ruin what looks like a reasonable correlation.
>>
>> Such a correlation can often be found, however:
>>
>>      o cyclomatic complexity is just one of many 'complexity'
>> metrics that have a high correlation with quantity of code,
>> so why not just measure lines of code?
>>
>>      o once developers know they are being judged by some metric
>> or other they can easily game the system by actions such as
>> splitting/merging functions.  If the metric has a causal connection
>> to the quantity of interest, e.g., faults, then everybody is happy
>> for developers to what what they will to reduce the metric,
>> but if the connection is simply a correlation (based on code
>> written by developers not trying to game the system) then
>> developers doing whatever it takes to improve the metric value
>> is at best wasted time.
>>
>>> -----Original Message-----
>>> From: Todd Carpenter <todd.carpenter at adventiumlabs.com>
>>> Date: Monday, June 24, 2013 7:20 PM
>>> To: "systemsafety at techfak.uni-bielefeld.de"
>>> <systemsafety at techfak.uni-bielefeld.de>
>>> Subject: Re: [SystemSafety] Qualifying SW as "proven in use"
>>> [Measuring	Software]
>>>
>>> ST> For example, the code quality measure "Cyclomatic Complexity"
>>> (reference:
>>> ST> Tom McCabe, ³A Complexity Measure², IEEE Transactions on Software
>>> ST> Engineering, December, 1976) was validated many years ago by simply
>>>
>>> DMJ> I am not aware of any study that validates this metric to a
>>> reasonable
>>> DMJ> standard.  There are a few studies that have used found a medium
>>> DMJ> correlation in a small number of data points.
>>>
>>> Les Hatton had an interesting presentation in '08, "The role of
>>> empiricism
>>> in improving the
>>> reliability of future software" that shows there is a strong correlation
>>> between
>>> source-lines-of-code and cyclomatic complexity, and that defects follow a
>>> power law distribution:
>>>
>>>
>>> http://www.leshatton.org/wp-content/uploads/2012/01/TAIC2008-29-08-2008.pd
>>> f
>>>
>>> Just another voice, which probably just adds evidence to the argument
>>> that
>>> we haven't yet found a
>>> trivial metric to predict bugs...
>>>
>>> -TC
>>>
>>> On 6/24/2013 6:38 PM, Derek M Jones wrote:
>>>> All,
>>>>
>>>>> Actually, getting the evidence isn't that tricky, it's just a lot of
>>>>> work.
>>>> This is true of most things (+ getting the money to do the work).
>>>>
>>>>> Essentially all one needs to do is to run a correlation analysis
>>>>> (correlation coefficient) between the proposed quality measure on the
>>>>> one
>>>>> hand, and defect tracking data on the other hand.
>>>> There is plenty of dirty data out there that needs to be cleaned up
>>>> before it can be used:
>>>>
>>>>
>>>> http://shape-of-code.coding-guidelines.com/2013/06/02/data-cleaning-the-n
>>>> e
>>>> xt-step-in-empirical-software-engineering/
>>>>
>>>>
>>>>> For example, the code quality measure "Cyclomatic Complexity"
>>>>> (reference:
>>>>> Tom McCabe, ³A Complexity Measure², IEEE Transactions on Software
>>>>> Engineering, December, 1976) was validated many years ago by simply
>>>> I am not aware of any study that validates this metric to a reasonable
>>>> standard.  There are a few studies that have used found a medium
>>>> correlation in a small number of data points.
>>>>
>>>> I have some data whose writeup is not yet available in a good enough
>>>> draft form to post to my blog.  I only plan to write about this
>>>> metric because it is widely cited and is long overdue for relegation
>>>> to the history of good ideas that did not stand the scrutiny of
>>>> empirical evidence.
>>>>
>>>>> finding a strong positive correlation between the cyclomatic complexity
>>>>> of
>>>>> functions and the number of defects that were logged against those same
>>>> Correlation is not causation.
>>>>
>>>> Cyclomatic complexity correlates well with lines of code, which
>>>> in turn correlates well with number of faults.
>>>>
>>>>> functions (I.e., code in that function needed to be changed in order to
>>>>> repair that defect).
>>>> Changing the function may increase the number of faults.  Creating two
>>>> functions where there was previously one will reduce an existing peak
>>>> in the distribution of values, but will it result in less faults
>>>> overall?
>>>>
>>>> All this stuff with looking for outlier metric values is pure hand
>>>> waving.  Where is the evidence that the reworked code is better not
>>>> worse?
>>>>
>>>>> According to one study of 18 production applications, code in functions
>>>>> with cyclomatic complexity <=5 was about 45% of the total code base but
>>>>> this code was responsible for only 12% of the defects logged against
>>>>> the
>>>>> total code base. On the other hand, code in functions with cyclomatic
>>>>> complexity of >=15 was only 11% of the code base but this same code was
>>>>> responsible for 43% of the total defects. On a per-line-of-code basis,
>>>>> functions with cyclomatic complexity >=15 have more than an order of
>>>>> magnitude increase in defect density over functions measuring <=5.
>>>>>
>>>>> What I find interesting, personally, is that complexity metrics for
>>>>> object-oriented software have been around for about 20 years and yet
>>>>> nobody (to my knowledge) has done any correlation analysis at all (or,
>>>>> at
>>>>> a minimum they have not published their results).
>>>>>
>>>>> The other thing to remember is that such measures consider only the
>>>>> "syntax" (structure) of the code. I consider this to be *necessary* for
>>>>> code quality, but far from *sufficient*. One also needs to consider the
>>>>> "semantics" (meaning) of that same code. For example, to what extent is
>>>>> the code based on reasonable abstractions? To what extent does the code
>>>>> exhibit good encapsulation? What are the cohesion and coupling of the
>>>>> code? Has the code used "design-to-invariants / design-forchange"? One
>>>>> can
>>>>> have code that's perfectly structured in a syntactic sense and yet it's
>>>>> garbage from the semantic perspective. Unfortunately, there isn't a way
>>>>> (that I'm aware of, anyway) to do the necessary semantic analysis in an
>>>>> automated fashion. Some other competent software professionals need to
>>>>> look at the code and assess it from the semantic perspective.
>>>>>
>>>>> So while I applaud efforts like SQALE and others like it, one needs to
>>>>> be
>>>>> careful that it's only a part of the whole story. More work--a lot
>>>>> more--needs to be done before someone can reasonably say that some
>>>>> particular code is "high quality".
>>>>>
>>>>>
>>>>> Regards,
>>>>>
>>>>> -- steve
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> -----Original Message-----
>>>>> From: Peter Bishop <pgb at adelard.com>
>>>>> Date: Friday, June 21, 2013 6:04 AM
>>>>> To: "systemsafety at techfak.uni-bielefeld.de"
>>>>> <systemsafety at techfak.uni-bielefeld.de>
>>>>> Subject: Re: [SystemSafety] Qualifying SW as "proven
>>>>> in    use"    [Measuring    Software]
>>>>>
>>>>> I agree with Derek
>>>>>
>>>>> Code quality is only a means to an end
>>>>> We need evidence to to show  the means actually helps to achieve the
>>>>> ends.
>>>>>
>>>>> Getting this evidence is pretty tricky, as parallel developments for
>>>>> the
>>>>> same project won't happen.
>>>>> But you might be able to infer something on average over multiple
>>>>> projects.
>>>>>
>>>>> Derek M Jones wrote:
>>>>>> Thierry,
>>>>>>
>>>>>>> To answer your questions:
>>>>>>> 1°) Yes, there is some objective evidence that there is a correlation
>>>>>>> between a low SQALE index and quality code.
>>>>>> How is the quality of code measured?
>>>>>>
>>>>>> Below you say that SQALE DEFINES what is "good quality" code.
>>>>>> In this case it is to be expected that a strong correlation will exist
>>>>>> between a low SQALE index and its own definition of quality.
>>>>>>
>>>>>>> For example ITRIS has conducted a study where the "good quality" code
>>>>>>> is statistically linked to a lower SQALE index, for industrial
>>>>>>> software actually used in operations.
>>>>>> Again how is quality measured?
>>>>>>
>>>>>>> No, there is not enough evidence, we wish there would be more people
>>>>>>> working on getting the evidence.
>>>>>> Is there any evidence apart from SQALE correlating with its own
>>>>>> measures?
>>>>>>
>>>>>> This is a general problem, lots of researchers create their own
>>>>>> definition of quality and don't show a causal connection to external
>>>>>> attributes such as faults or subsequent costs.
>>>>>>
>>>>>> Without running parallel development efforts that
>>>>>> follow/don't follow the guidelines it is difficult to see how
>>>>>> reliable data can be obtained.
>>>>>>
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