Software testing optimization through test suite reduction using fuzzy clustering

Abstract

Software testing is a most important but expensive activity. To get the most efficient and effective testing, test cases are designed on the basis of conditions. While designing test cases, many test cases are developed that are of no use or produced in duplicate. Exhaustive testing requires program execution with all possible combinations of values for program variables, which is impractical due to resource limitations. Redundant test cases or the test cases that are of no use, simply increases the testing effort and hence increases the cost. Our goal is to reduce the time spent in testing by reducing the number of test cases. For this we have incorporated fuzzy techniques to reduce the number of test cases so that more efficient and accurate results may be achieved. Fuzzy clustering is a class of algorithms for cluster analysis in which the allocation of similar test cases is done to clusters that would help in finding out redundancy incorporated by test cases. We proposed a methodology based on fuzzy clustering by which we can significantly reduce the test suite. The final test suite resulted from methodology will yield good results for conditions/path coverage.