Software Fault Localization Based on SALSA Algorithm
Xin Fan, Zuxiong Shen, Zhenlei Fu, Yun Ge · Applied Sciences · 2025
In software development, debugging is the most tedious and time-consuming phase. Therefore, various automated fault localization techniques have been proposed to assist debugging. Among existing fault localization techniques, Spectrum-Based Fault Localization (SBFL) is one of the most extensively researched methods. Traditional SBFL techniques rely solely on the coverage of program execution for fault localization, which means they neglect the interactions between program entities and fault propagation paths during the execution of the program, resulting in a tie problem that reduces the accuracy of fault localization. To solve the above problem, this paper proposes SA-SBFL, a fault localization method based on the SALSA (Random Method for Link Structure Analysis) algorithm. First, a link graph of program entities is constructed, which includes interactions between program entities and fault propagation paths. Then, the suspicion values obtained from traditional SBFL methods are used as the initial weights of the link graph. Finally, the random walk model is employed to simulate the propagation of faults among program entities, analyze the importance of program entities in the fault propagation process, and obtain a ranking list of suspicious program entities. The experiments in this paper demonstrate that the SA-SBFL method significantly outperforms general SBFL methods. For instance, in the Defects4J dataset, the SA-SBFL technique outperforms traditional SBFL in terms of fault localization accuracy, with a 47% improvement in the Top-1 metric and a 10% increase in the Top-5 metric, and it also showed an average improvement of 19% in the EXAM metric.