Enhancing Fault Localization by incorporating Statement Frequency and Test Case Contribution

Fault Localization (FL) is the key activity while debugging a program. Any improvement to this activity leads reduction in total software development cost. Spectrum-based fault localization (SBFL) techniques are considered to be the most prominent for FL because of their scalability and efficiency. However, these techniques suffer from the problem of ties, focus only on the binary coverage (0/1) information of program elements, and have a huge scope for improvement in their effectiveness. Also, the test suites available for FL have a biased number of pass and failed test cases. In order to solve these issues, we proposed a novel fault localization technique in this paper. Our proposed technique uses statement frequency information and considers individual test case contributions to assign different suspiciousness scores to the statements and generates an effective ranked list of test cases by balancing the test cases. Experimental results show that the proposed method performs on average 42.48% better than other contemporary FL methods such as $\mathrm{D}^{\ast}$, Tarantula, Ochiai, Ample, Barinel, BPNN, RBFNN, and Crosstab.