Relationship between the Effectiveness of Spectrum-Based Fault Localization and Bug-Fix Types in JavaScript Programs
| Autor: | Attila Szatmari, Arpad Beszedes, Bela Vancsics |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
021103 operations research
Source code Java business.industry Computer science media_common.quotation_subject Significant difference 0211 other engineering and technologies 020207 software engineering Classification scheme 02 engineering and technology Fault (power engineering) JavaScript Machine learning computer.software_genre Software fault 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Artificial intelligence business computer media_common computer.programming_language |
| Zdroj: | SANER |
| DOI: | 10.1109/saner48275.2020.9054803 |
| Popis: | Spectrum-Based Fault Localization (SBFL) is a well-understood statistical approach to software fault localization, and there have been numerous studies performed that tackle its effectiveness. However, mostly Java and C/C++ programs have been addressed to date. We performed an empirical study on SBFL for JavaScript programs using a recent bug benchmark, BugsJS. In particular, we examined (1) how well some of the most popular SBFL algorithms, Tarantula, Ochiai and DStar, can predict the faulty source code elements in these JavaScript programs, (2) whether there is a significant difference between the effectiveness of the different SBFL algorithms, and (3) whether there is any relationship between the bug-fix types and the performance of SBFL methods. For the latter, we performed a manual classification of each benchmark bug according to an existing classification scheme. Results show that the performance of the SBFL algorithms is similar but there are some notable differences among them as well, and that certain bug-fix types can be significantly differentiated from the others (in both positive and negative direction) based on the fault localization effectiveness of the investigated algorithms. |
| Databáze: | OpenAIRE |
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