Spectrum-Based Fault Localization via Enlarging Non-Fault Region to Improve Fault Absolute Ranking

Spectrum-based fault localization (<italic>SFL</italic>) is a popular lightweight automatic software fault localization technique that uses coverage information of program execution to compute the likelihood of root cause of failure(s) for each program component and ranks them descending by their suspiciousness scores. However, some recent studies indicate an <italic>SFL</italic> technique to be useful only if the root cause(s) of failures is ranked at <italic>top k</italic>. Due to the nature of the <italic>SFL</italic> technique, it is impossible that the root fault(s) is always ranked at <italic>top k</italic>, which may interfere with the usefulness of <italic>SFL</italic> in practice. To solve this issue, an <italic>SFL</italic> technique via enlarging the non-fault region to further improve fault absolute ranking was proposed. The idea behind this is that we can intuitively improve fault absolute ranking for an <italic>SFL</italic> technique if some non-fault components ranked higher were excluded from the fault ranking list. In the approach, we enlarge the non-fault region iteratively to narrow down the suspicious region based on two scenarios, and then rank those components in the suspicious region using existing <italic>SFL</italic> techniques. The empirical results indicate that our approach significantly helps existing <italic>SFL</italic> techniques to further improve their usefulness.