A Comparative Evaluation of Test Coverage Techniques Effectiveness

Abstract

Software systems have become complex and challenging to develop and maintain because of the large size of test cases with increased scalability issues. Test case prioritization methods have been successfully utilized in test case management. However, the prohibitively exorbitant cost of large test cases is now the mainstream in the software industry. The growth of agile test-driven development has increased the expectations for software quality. Yet, our knowledge of when to use various path testing criteria for cost-effectiveness is inadequate due to the inherent complexity in software testing. Existing researches attempted to address the issue without effectively tackling the scalability of large test suites to reduce time in regression testing. In order to provide a more accurate way of fault detection in software projects, we introduced novel coverage criteria, called Incremental Cluster-based test case Prioritization (ICP), and investigated its potentials by making a comparative evaluation with three un-clustered traditional coverage-based criteria: Prime-Path Coverage (PPC), Edge-Pair Coverage (EPC) and Edge Coverage (EC) based on mutation analysis. By clustering test suites, based on their dynamic run-time behavior, the number of pair-wise comparisons is reduced significantly. To compare, we analyzed 20 functions from 25 C programs, instrumented faults into the programs, and used the Mull mutation tool to generate mutants and perform a statistical analysis of the results. The experimental results show that ICP can lead to cost-effective improvements in fault detection.