Fault Localization Using TrustRank Algorithm

作者：Fan, Xin; Wu, Kaisheng^*; Zhang, Shuqing; Yu, Li; Zheng, Wei; Ge, Yun

来源：Applied Sciences-Basel, 2023, 13(22): 12344.

DOI：10.3390/app132212344

• fault localization
• program spectra
• complex networks
• data dependency
• TrustRank algorithm

Spectrum-based fault localization (SBFL), a widely recognized technique in automated fault localization, has limited effectiveness due to its disregard for the internal information of the program under test suites. To overcome this limitation, a novel TrustRank-based fault localization (TRFL) technique is introduced. TRFL enhances traditional SBFL by incorporating internal data dependencies of the program under the test suite, thereby providing a more comprehensive analysis. It constructs a node-weighted program execution network and employs the TrustRank algorithm to analyze network centrality and re-rank program entities based on their suspiciousness. Furthermore, a bidirectional TrustRank algorithm (Bi-TRFL) is extended that takes into account the influence relationship between network nodes for more accurate fault localization. When applied to large-scale datasets with real faults, such as Defects4J, TRFL, and Bi-TRFL, it significantly outperforms traditional SBFL methods in fault localization. They demonstrate up to 40% and 13% improvement in Top-1 and Top-5 rankings, respectively, proving their robustness and efficiency with minimal sensitivity to related parameters.

• 单位
  南昌航空大学; 南京航空航天大学