Chiang Mai Journal of Science

             The target of software engineering is to produce high quality software product at low cost. Software testing consists of three main activities: selecting tests cases, execution of test cases on the software under test, and evaluating the correctness of the outputs. The first and third of these activities are labour-intensive, ambiguous and error prone. How to provide cost-effective strategies for software test cases classification, minimization and selection has been one of the research focuses in software testing for a long time. Many researchers and academicians have addressed the effectiveness/fitness, selection, classification, minimization of software test cases, and obtained many interesting results. Quality of software testing is low due to uncertainty & inadequate techniques for estimating the fitness, classification, selection of test cases and requires improvement. Test cases fitness depends on several parameters. Vagueness of fitness of test cases and their fitness parameters have created the uncertainty in classification and selection of test cases. However, one issue of paramount importance in software testing i.e. the intrinsic imprecise and uncertainty of test cases fitness, fitness parameters, multi-objective classification and selection, is left unaddressed. However, by applying appropriate test case classification, selection techniques, testing efforts can be reduced considerably. Moreover, by using the multi-faceted classification, selection of test cases with test data adequacy criteria will help in improving the overall quality of the software. Fuzzy logic based multi-faceted measurement framework will be the solution to the problem of test cases fitness evaluation  on multiple parameters, and  multi-objective test case classification, selection problem. Present paper gives two contributions to software testing research first uncertainty in software testing and secondly insight into multi-faceted measurement framework for test cases classification and fitness evaluation using fuzzy logic based approach.