Random or evolutionary search for object‐oriented test suite generation?

Abstract

Summary An important aim in software testing is constructing a test suite with high structural code coverage, that is, ensuring that most if not all of the code under test have been executed by the test cases comprising the test suite. Several search‐based techniques have proved successful at automatically generating tests that achieve high coverage. However, despite the well‐established arguments behind using evolutionary search algorithms (eg, genetic algorithms) in preference to random search, it remains an open question whether the benefits can actually be observed in practice when generating unit test suites for object‐oriented classes. In this paper, we report an empirical study on the effects of using evolutionary algorithms (including a genetic algorithm and chemical reaction optimization) to generate test suites, compared with generating test suites incrementally with random search. We apply the EVOSUITE unit test suite generator to 1000 classes randomly selected from the SF110 corpus of open‐source projects. Surprisingly, the results show that the difference is much smaller than one might expect: While evolutionary search covers more branches of the type where standard fitness functions provide guidance, we observed that, in practice, the vast majority of branches do not provide any guidance to the search. These results suggest that, although evolutionary algorithms are more effective at covering complex branches, a random search may suffice to achieve high coverage of most object‐oriented classes.

Using Relative Lines of Code to Guide Automated Test Generation for Python

“…More generally, one overall take-away from this paper should be that test generation heuristics are not equally effective for all SUTs and test harnesses; rather, performance varies widely by the structure of the input and state space. This is not a novel observation, of course [98,99]. In a sense, this goes with the territory of heuristics, vs. mathematically proven optimizations of testing (alas, the latter are rarely possible) [47,51].…”

Effective and efficient API misuse detection via exception propagation and search-based testing

“…Another threat is related to the parameter setting of the search algorithms. We used the parameter values suggested by the related literature [7,43,53].…”

Enabledness-based Testing of Object Protocols

“…A noteworthy tool for generating call sequences is Randoop [70], a purely random (and black box) approach that generates method sequences by reusing previously returned values and discarding sequences that terminate abnormally (i.e., exceptions). Although this increases the likelihood of reaching "deep" states within the object protocol, genetic algorithms aiming at structural coverage can outperform Randoop (e.g., [40,84]) at failure detection.…”