Prioritizing tests for fault localization through ambiguity group reduction

In practically all development processes, regression tests are used to detect the presence of faults after a modification. If faults are detected, a fault localization algorithm can be used to reduce the manual inspection cost. However, while using test case prioritization to enhance the rate of fault detection of the test suite (e.g., statement coverage), the diagnostic information gain per test is not optimal, which results in needless inspection cost during diagnosis. We present RAPTOR, a test prioritization algorithm for fault localization, based on reducing the similarity between statement execution patterns as the testing progresses. Unlike previous diagnostic prioritization algorithms, RAPTOR does not require false negative information, and is much less complex. Experimental results from the Software Infrastructure Repository's benchmarks show that RAPTOR is the best technique under realistic conditions, with average cost reductions of 40% with respect to the next best technique, with negligible impact on fault detection capability.