An empirical study on clustering for isolating bugs in fault localization

Abstract

Spectrum-based Fault Localization (SBFL) techniques use risk evaluation formulas to calculate each statement's likelihood of having a bug based on test results. SBFL can not only be used in statement level, but also can be used with other program entities such as branches, functions and so on. Most previous studies have been conducted under the assumption of a single bug. However, software always contains multi-bugs in practice. A natural idea of debugging is to isolate bugs and then use SBFL techniques to locate one bug for each group. In this paper, we conduct an empirical study on clustering for isolating bugs in fault localization. We analyze the effects of six fault localization techniques and two cluster algorithms. The main observations are: (1) ER5 (Wong1) achieves the best results of fault localization with clustering; (2) K-means outperforms hierarchical clustering for isolating bugs in fault localization.