A Systematic Review of the Application and Empirical Investigation of Search-based Test-Case Generation

Written by: S. Ali, L. Briand, H. Hemmati, and R. K. Panesar-Walawege. IEEE Transactions on Software Engineering (TSE), vol 36, no 6, pp 742-762, Nov/Dec, 2010.


Metaheuristic search techniques have been extensively used to automate the process of generating test cases and thus providing solutions for a more cost-effective testing process. This approach to test automation, often coined as “Search-based Software Testing” (SBST), has been used for a wide variety of test case generation purposes, differing in terms of test objectives, test levels, and other characteristics [1]. Since SBST techniques are heuristic by nature, they must be empirically investigated in terms of how costly and effective they are at reaching their test objectives and whether they scale up to realistic development artifacts. However, approaches to empirically study SBST techniques have shown wide variation in the literature. This paper presents the results of a systematic, comprehensive review that aims at characterizing how empirical studies have been designed to investigate SBST cost-effectiveness and what empirical evidence is available in the literature regarding SBST cost-effectiveness and scalability. We also provide a framework that drives the data collection process of this systematic review and can be the starting point of guidelines on how SBST techniques can be empirically assessed. The intent is to aid future researchers doing empirical studies in SBST by providing an unbiased view of the body of empirical evidence and by guiding them in performing well designed and executed empirical studies.
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