Spectrum Based Fault Localization (SBFL) uses different metrics called risk evaluation formula to guide and pinpoint faults in debugging process. The accuracy of a specific SBFL method may be limited by the used formulae and program spectra. However, it has been demonstrated recently that Genetic Programming could be used to automatically design formulae directly from the program spectra. Therefore, this article presents Genetic Programming approach for proposing risk evaluation formula with the inclusion of radicals to evolve suspiciousness metric directly from the program spectra. 92 faults from Unix utilities of SIR repository and 357 real faults from Defect4J repository were used. The approach combines these data sets, used 25% of the total faults (113) to evolve the formulae and the remaining 75% (336) to validate the effectiveness of the metrics generated by our approach. The proposed approach then uses Genetic Programming to run 30 evolution to produce different 30 metrics. The GP-generated metrics consistently out-performed all the classic formulae in both single and multiple faults, especially OP2 on average of 2.25% in single faults and 3.42% in multiple faults. The experiment results conclude that the combination of Hybrid data set and radical is a good technique to evolve effective formulae for spectra-based fault localization.

Spectrum-Based Fault Localization (SBFL) is one of the most effective fault localization techniques, and its performance closely depends on the program spectra and the ranking formula. Despite the numerous proposed approaches for fault localization, there are still great demands for fault localization techniques that can help guide developers to the locations of faults. Therefore, this paper defines four metrics from the program spectrum, which can become essential components of ranking formulas to mitigate spectrum-based fault localization problems. These metrics are further combined to propose a new heuristic, Metrics Combination (MECO), which does not require any prior information on program structure or semantics to locate faults effectively. The evaluation experiments are conducted on the Defects4J and SIR datasets, and MECO is compared with the 18 maximal formulas. The experimental result shows that MECO is more efficient in terms of Precision, Accuracy, and Wasted Efforts than the compared formulas. An empirical evaluation also indicates that two of the defined metrics, Assumption Proportion and Fault Assumption, when combined with the existing formulas, improve the localization effectiveness, especially the precision of ER5a-c (77.77%), GP02 (41%), and GP19 (27.22%), respectively.

Spectrum-based fault localization (SBFL) is an automated fault localization technique that uses risk evaluation metrics to compute the suspiciousness scores from program spectra. Thus, risk evaluation metrics determine the technique’s performance. However, the existing experimental studies still show no optimal metric for different program structures and error types. It is possible to further optimize SBFL’s performance by combining different metrics. Therefore, this paper effectively explores the combination of risk evaluation metrics for precise fault localization. Based on extensive experiments using 92 faults from SIR and 357 faults from Defects4J repositories, we highlight what and which risk evaluation metrics to combine to maximize the efficiency and accuracy of fault localization. The experimental results show that combining risk evaluation metrics with high negative correlation values can improve fault localization effectiveness. Similarly, even though the combination of positively correlated effective risk evaluation metrics can outperform most negatively correlated non-effective ones, it still cannot improve the fault localization effectiveness. Furthermore, low-correlated risk evaluation metrics should also be considered for fault localization. The study concluded that getting highly negatively correlated risk evaluation metrics is almost impossible. The combination of such risk evaluation metrics would improve fault localization accuracy.