Leveraging Contextual Information from Function Call Chains to Improve Fault Localization
| Autor: | Arpad Beszedes, Tibor Gyimóthy, Massimiliano Di Penta, Ferenc Horváth |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
021103 operations research
Call stack Computer science Subroutine 0211 other engineering and technologies 020207 software engineering 02 engineering and technology Fault (power engineering) computer.software_genre Ranking 020204 information systems 0202 electrical engineering electronic engineering information engineering Code (cryptography) Contextual information Data mining Element (category theory) Programmer computer Complement (set theory) |
| Zdroj: | SANER ICSE (Companion Volume) |
| DOI: | 10.1109/saner48275.2020.9054820 |
| Popis: | In Spectrum Based Fault Localization, program elements such as statements or functions are ranked according to a suspiciousness score which can guide the programmer in finding the fault more efficiently. However, such a ranking does not include any additional information about the suspicious code elements. In this work, we propose to complement function-level spectrum based fault localization with function call chains - i.e., snapshots of the call stack occurring during execution - on which the fault localization is first performed, and then narrowed down to functions. Our experiments using defects from Defects4J show that (i) 69% of the defective functions can be found in call chains with highest scores, (ii) in 4 out of 6 cases the proposed approach can improve Ochiai ranking of 1 to 9 positions on average, with a relative improvement of 19–48%, and (iii) the improvement is substantial (66–98%) when Ochiai produces bad rankings for the faulty functions. |
| Databáze: | OpenAIRE |
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