Fuzzy entropy-based framework for multi-faceted test case classification and selection: an empirical study

摘要

Software testing is complex, ambiguous, labour-intensive, costly, error prone and a core activity of software development. Devising the cost-effective and adequate strategies for software test cases optimisation has been one of the research issues in software testing for a long time. Existing techniques of test case optimisation are not providing the optimal solution to the test cases optimisation problem in terms of precision, completeness, cost and adequacy. The authors have already proposed a fuzzy logic-based multi-faceted measurement framework for test cases classification and fitness evaluation. Though, it reduces testing efforts, cost, incompleteness and increases adequacy, but, still there is ambiguity in classification and selection of some test cases due to ambiguity in fitness of test cases. Hence, there is a strong need to devise a technique to measure suitably and resolve the ambiguity in software test cases classification and selection problem. In this paper, the authors have unified their earlier proposed framework by introducing fuzzy entropy-based approach. The proposed unified framework chunks out the high ambiguity test cases and selects low ambiguity test cases for exercising on SUT (Software under Test). The proposed unified framework is tested on artefacts of benchmark applications, and the results show that the proposed unified framework enhances the classification accuracy by reducing ambiguity, and increases the number of test cases classified accurately.