Fault Localizations Through Feature Selections
| Autor: | Xiaozhen Xue, Akbar Siami Namin, Yulei Pang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer Networks and Communications
Computer science media_common.quotation_subject 020207 software engineering 02 engineering and technology Longest increasing subsequence Fault (power engineering) computer.software_genre 01 natural sciences Computer Graphics and Computer-Aided Design Ranking (information retrieval) 010104 statistics & probability Test case Debugging Artificial Intelligence Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Data mining 0101 mathematics computer Software Feature ranking media_common |
| Zdroj: | International Journal of Software Engineering and Knowledge Engineering. 27:1269-1289 |
| ISSN: | 1793-6403 0218-1940 |
| DOI: | 10.1142/s0218194017500474 |
| Popis: | We introduce a novel application of feature ranking methods to the fault localization problem. We envision the problem of localizing causes of failures as instances of ranking program’s elements where elements are conceptualized as features. In this paper, we define features as program’s statements. However, in its fine-grained definition, the idea of program’s features can refer to any traits of programs. This paper proposes feature ranking-based algorithms. The algorithms analyze execution traces of both passing and failing test cases, and extract the bug signatures from the failing test cases. The proposed procedure extracts possible combinations of program’s elements when executed together from bug signatures. The feature ranking-based algorithms then order statements according to the suspiciousness of the combinations. When viewed as sequences, the combination of program’s elements produced and traced in bug signatures can be utilized to reason about the common longest subsequence. The common longest subsequence of bug signatures represents the common statements executed by all failing test cases and thus provides a means for identifying statements that contain possible faults. Our evaluation indicates that the proposed feature-based fault localization outperforms existing fault localization ranking schemes. |
| Databáze: | OpenAIRE |
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