Name
Abstract | ||
|---|---|---|
Testing and debugging are very important tasks in software development. Fault localization is a very critical activity in the debugging process and also is one of the most difficult and time-consuming activities. The demand for effective fault localization techniques that can aid developers to the location of faults is high. In this paper, a fault localization technique based on complex network theory named FLCN-S is proposed to improve localization effectiveness on single-fault subject programs. The proposed technique diagnoses and ranks faulty program statements based on their behavioral anomalies and distance between statements in failed tests execution by utilizing two network centrality measures (degree centrality and closeness centrality). The proposed technique is evaluated on a well-known standard benchmark (Siemens test suite) and four Unix real-life utility subject programs (gzip, sed, flex, and grep). Overall, the results show that FLCN-S is significantly more effective in locating faults in comparison with other techniques. Furthermore, we observed that both degree and closeness centrality play a vital role in the identification of faults. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.array.2019.100008 | Array |
Keywords | DocType | Volume |
Complex network,Fault localization,Program debugging,Program spectra,Software testing | Journal | 3 |
ISSN | Citations | PageRank |
2590-0056 | 1 | 0.39 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abubakar Zakari | 1 | 2 | 2.10 |
| Sai Peck Lee | 2 | 142 | 22.55 |
| Ibrahim Abaker Targio Hashem | 3 | 1 | 0.39 |