Mahadevan Subramaniam and Parvathi Chundi
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Software testing, data clustering, regression, observation testing
Clustering test profiles to retrieve relevant tests is a recurring theme in software validation. A novel clustering approach using a probabilistic notion of coverage among line-based test profiles is described. The approach automatically determines the number of clusters to generate a clustering and can potentially group together tests to execute a few distinct lines of code. A simple method to identify tests affected by program changes is developed and used to determine the retrieval effort needed for cluster-based retrieval of affected tests. The approach is applied to four unix utility programs from a popular testing benchmark. Our results show that comparing original and new clusterings with respect to test cases is a promising criterion for deciding the potential re-clustering points in software evolution.
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