Adaptive Evolutionary Testing: An Adaptive Approach to Search-Based Test Case Generation for Object-Oriented Software

Abstract

Adaptive Evolutionary Algorithms are distinguished by their dynamic manipulation of selected parameters during the course of evolving a problem solution; they have an advantage over their static counterparts in that they are more reactive to the unanticipated particulars of the problem. This paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an Adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the test case generation algorithm's efficiency considerably, while introducing a negligible computational overhead.

Object-Oriented Evolutionary Testing

“…Ribeiro et al also employed STGP for representing test programs, and presented a series of studies on defining strategies for addressing the challenges posed by the OO paradigm, which include methodologies for: (i) systematizing both the test object analysis and the test data generation Ribeiro, 2008) processes; (ii) introducing an input domain reduction methodology, based on the concept of purity analysis, which allows the identification and removal of entries that are irrelevant to the search problem because they do not contribute to the definition of relevant test scenarios (Ribeiro, Zenha-Rela, & Vega, 2008;Ribeiro, Zenha-Rela, & Vega, 2009); (iii) proposing an adaptive strategy for promoting the introduction of relevant instructions into the generated test cases by means of mutation, which utilizes Adaptive EAs (Ribeiro, Zenha-Rela, & Vega, 2010a); and (iv) defining an object reuse methodology for GP-based approaches to ET, which allows one object instance can be passed to multiple methods as an argument (or multiple times to the same method as arguments) and enables the generation of test programs that exercise structures of the software under test that would not be reachable otherwise (Ribeiro, Zenha-Rela, & Vega, 2010b).…”