A Systematic Literature Mapping of Artificial Intelligence Planning in Software Testing

Luis F. de Lima, Leticia Peres, André Grégio, Fabiano Silva

Abstract

Software testing is one of the most expensive software development processes. So, techniques to automate this process are fundamental to reduce software cost and development time. Artificial intelligence (AI) planning technique has been applied to automate part of the software testing process. We present in this paper a systematic literature mapping (SLM), using Petersen et al. (2015) approach of methods, techniques and tools regarding AI planning in software testing. Using the mapping, we identify 16 papers containing methods, techniques, frameworks and tools proposals, besides a survey. We identify testing techniques, testing phases, artifacts, AI planning techniques, AI planning tools, support tools, and generated plans in these selected papers. By mapping data analyses we identify a deficiency in the use of white-box and error-based testing techniques, besides the recent use of AI planning in security testing.

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