# Fault Localization Using TrustRank Algorithm
## 3 Proposed Approach
### Data Pre-Processing
#### 3.1.1 Program Fault Localization Using Traditional SBFL Techniques
This new algorithm considers both the

Figure 7: Impact of decay factor a (**a**) of TrustRank algorithm and decay factor \(\gamma\) (**b**) of bidirectional TrustRank on the improvement of EXAM. propagation impact of error nodes and the positive impact of the trustworthy nodes on error suspicion. Through this method, a suspicious list is finally obtained to assist researchers in locating fault locations and improving the ranking of error elements in the list. To verify the effectiveness of this method, five cases of the open-source benchmark programs were studied and compared with six related suspicion calculation formulas. The experimental results show that compared with traditional SBFL localization methods, our method significantly outperforms existing state-of-the-art SBFL techniques and can significantly narrow down code inspection range, especially within the high-performance range (i.e., top 5% elements and Top-N), thereby significantly improving software fault localization performance. Formulation or evolution of overarching research goals and aims and management and coordination responsibility for the research activity planning and execution, X.F. ; application of statistical, mathematical, computational, or other formal techniques to analyze or synthesize study data, K.W. ; application of statistical, mathematical, computational, or other formal techniques to analyze or synthesize study data, K.W. and S.Z. ; oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team and provision of study materials, reagents, materials, patients, laboratory samples, animals, instrumentation, computing resources, or other analysis tools, L.Y., W.Z. and Y.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant/Award No. 61867004) and the Youth Fund of the National Natural Science Foundation of China (Grant/Award No. 42261070). The data that support the findings of this study are openly available at [[https://github.com/rjust/defects4j](https://github.com/rjust/defects4j)]([https://github.com/rjust/defects4j](https://github.com/rjust/defects4j)) (accessed on 13 April 2023). The authors declare that there are no conflict of interest regarding the publication of this paper. ## References

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