Lionel Briand and his group at the Simula Research Laboratory in Oslo have helped raise the standard for empirical research in testing and other software engineering practices by criticizing work that in their opinion relies on wrong assumptions or insufficiently supported evidence. In one of their latest papers  they take aim at “Adaptive Random Testing” (ART); one of the papers they criticize is from our group at ETH, on the ARTOO extension  to this testing method. Let’s examine the criticism!
We need a bit of background on random testing, ART, and ARTOO:
- Random testing tries inputs based on a random process rather than attempting a more sophisticated strategy; it was once derided as silly , but has emerged in recent years as a useful technique. Our AutoTest tool , now integrated in EiffelStudio, has shown it to be particularly effective when applied to code equipped with contracts, which provide built-in test oracles. As a result of this combination, testing can be truly automatic: the two most tedious tasks of traditional testing, test case preparation and test oracle definition, can be performed without human intervention.
- ART, developed by Chen and others , makes random testing not entirely random by ensuring that the inputs are spread reasonably evenly in the input domain.
- ARTOO, part of Ilinca Ciupa’s PhD thesis on testing defended in 2008, generalized ART to object-oriented programs, by defining a notion of distance between objects; the ARTOO strategy avoids choosing objects that are too close to each other. The distance formula, which you can find in, combines three elementary distances: between the types of the objects involved, the values in their primitive fields (integers etc.), and, recursively, the objects to which they have references.
Arcuri and Briand dispute the effectiveness of ART and criticize arguments that various papers have used to show its effectiveness. About the ARTOO paper they write
The authors concluded that ART was better than random testing since it needed to sample less test cases before finding the first failure. However, ART was also reported as taking on average 1.6 times longer due to the distance calculations!
To someone not having read our paper the comment and the exclamation mark would seem to suggest that the paper somehow downplays this property of random testing, but in fact it stresses it repeatedly. The property appears for example in boldface as part of the caption to Table 2: In most cases ARTOO requires significantly less tests to find a fault, but entails a time overhead, and again in boldface in the caption to Table 3: The overhead that the distance calculations introduce in the testing process causes ARTOO to require on average 1.6 times more time than RAND to find the first fault.
There is no reason, then, to criticize the paper on this point. It reports the results clearly and fairly.
If we move the focus from the paper to the method, however, Arcuri and Briand have a point. As they correctly indicate, the number of tests to first fault is not a particularly useful criterion. In fact I argued against it in another paper on testing 
The number of tests is not that useful to managers, who need help deciding when to stop testing and ship, or to customers, who need an estimate of fault densities. More relevant is the testing time needed to uncover the faults. Otherwise we risk favoring strategies that uncover a failure quickly but only after a lengthy process of devising the test; what counts is total time. This is why, just as flies get out faster than bees, a seemingly dumb strategy such as random testing might be better overall.
(To understand the mention of flies and bees you need to read .) The same article states, as its final principle:
Principle 7: Assessment criteria A testing strategy’s most important property is the number of faults it uncovers as a function of time.
The ARTOO paper, which appeared early in our testing work, used “time to first failure” because it has long been a standard criterion in the testing literature, but it should have applied our own advice and focused on more important properties of testing strategies.
The “principles” paper  also warned against a risk awaiting anyone looking for new test strategies:
Testing research is vulnerable to a risky thought process: You hit upon an idea that seemingly promises improvements and follow your intuition. Testing is tricky; not all clever ideas prove helpful when submitted to objective evaluation.
The danger is that the clever ideas may result in so much strategy setup time that any benefit on the rest of the testing process is lost. This danger threatens testing researchers, including those who are aware of it.
The idea of ARTOO and object distance remains attractive, but more work is needed to make it an effective contributor to automated random testing and demonstrate that effectiveness. We can be grateful to Arcuri and Briand for their criticism, and I hope they continue to apply their iconoclastic zeal to empirical software engineering work, ours included.
I have objections of my own to their method. They write that “all the work in the literature is based either on simulations or case studies with unreasonably high failure rates”. This is incorrect for our work, which does not use simulations, relying instead on actual, delivered software, where AutoTest routinely finds faults in an automatic manner.
In contrast, however, Arcuri and Briand rely on fault seeding (also known as fault introduction or fault injection):
To obtain more information on how shapes appear in actual SUT, we carried out a large empirical analysis on 11 programs. For each program, a series of mutants were generated to introduce faults in these programs in a systematic way. Faults generated through mutation [allow] us to generate a large number of faults, in an unbiased and varied manner. We generated 3727 mutants and selected the 780 of them with lower detection probabilities to carry out our empirical analysis of faulty region shapes.
In the absence of objective evidence attesting to the realism of fault seeding, I do not believe any insights into testing obtained from such a methodology. In fact we adopted, from the start of our testing work, the principle that we would never rely on fault seeding. The problem with seeded faults is that there is no guarantee they reflect the true faults that programmers make, especially the significant ones. Techniques for fault seeding are understandably good at introducing typographical mistakes, such as a misspelling or the replacement of a “+” by a “-”; but these are not interesting kinds of fault, as they are easily caught by the compiler, by inspection, by low-tech static tools, or by simple tests. Interesting faults are those resulting from a logical error in the programmer’s mind, and in my experience (I do not know of good empirical studies on this topic) seeding techniques do not generate them.
For these reasons, all our testing research has worked on real software, and all the faults that AutoTest has found were real faults, resulting from a programmer’s mistake.
We can only apply this principle because we work with software equipped with contracts, where faults will be detected through the automatic oracle of a violated assertion clause. It is essential, however, to the credibility and practicality of any testing strategy; until I see evidence to the contrary, I will continue to disbelieve any testing insights resulting from studies based on artificial fault injection.
 Andrea Arcuri and Lionel Briand: Adaptive Random Testing: An Illusion of Effectiveness, in ISSTA 2011 (International Symposium on Software Testing and Analysis), available here.
 Ilinca Ciupa, Andreas Leitner, Manuel Oriol and Bertrand Meyer: ARTOO: Adaptive Random Testing for Object-Oriented Software, in ICSE 2008: Proceedings of 30th International Conference on Software Engineering, Leipzig, 10-18 May 2008, IEEE Computer Society Press, 2008, also available here.
 Glenford J. Myers. The Art of Software Testing. Wiley, New York, 1979. Citation:
Probably the poorest methodology of all is random-input testing: the process of testing a program by selecting, at random, some subset of all possible input values. In terms of the probability of detecting the most errors, a randomly selected collection of test cases has little chance of being an optimal, or close to optimal, subset. What we look for is a set of thought processes that allow one to select a set of test data more intelligently. Exhaustive black-box and white-box testing are, in general, impossible, but a reasonable testing strategy might use elements of both. One can develop a reasonably rigorous test by using certain black-box-oriented test-case-design methodologies and then supplementing these test cases by examining the logic of the program (i.e., using white-box methods).
 Bertrand Meyer, Ilinca Ciupa, Andreas Leitner, Arno Fiva, Yi Wei and Emmanuel Stapf: Programs that Test Themselves, IEEE Computer, vol. 42, no. 9, pages 46-55, September 2009, available here. For practical uses of AutoTest within EiffelStudio see here.
 T. Y. Chen, H Leung and I K Mak: Adaptive Random Testing, in Advances in Computer Science, ASIAN 2004, Higher-Level Decision Making, ed. M.J. Maher, Lecture Notes in Computer Science 3321, Springer-Verlag, pages 320-329, 2004, available here.
 Bertrand Meyer: Seven Principles of Software testing, in IEEE Computer, vol. 41, no. 10, pages 99-101, August 2008, also available here.