Feature-FL: Feature-Based Fault Localization

Yan Lei

doi:10.1109/TR.2022.3140453

2022-03-01

PRE

DOI：10.1109/TR.2022.3140453

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摘要

En 中文

Fault localization aims at developing an effective methodology identifying suspicious statements potentially responsible for program failures. The spectrum-based fault localization is the widely used methodology by analyzing the statistical coincidences viewed from the spectrum to evaluate the suspiciousness of each statement of being faulty. However, just analyzing statistical coincidences in the coverage information perspective and without combining diverse amount of information may restrict fault localization effectiveness. Thus, this article proposes feature-based fault localization (Feature-FL): A family fault localization methodology of feature-based metrics by combining the feature diversity from the view of program features into suspiciousness evaluation. Specifically, Feature-FL defines a concept of branching execution probability to abstract program behaviors as the values of features. Then, Feature-FL uses feature selection (i.e., a family of feature-based metrics) to evaluate the relevance of each feature with program failures. Finally, Feature-FL associates each feature with its corresponding statement, and uses the relevance as the suspiciousness to locate suspicious statements. We present six feature-based metrics for Feature-FL, and conduct an extensive study to evaluate the effectiveness of Feature-FL and its potential over the state-of-the-art spectrum-based formulas. Our results provide insight into the potential among different feature-based metrics and also show Feature-FL significantly outperforms the state-of-the-art spectrum-based formulas, e.g., an average saving of at least 30% over spectrum-based formulas in case of real faults.

Keyword:

Location awareness

Feature extraction

Correlation

Debugging

Data models

Codes

Computer bugs

Execution probability

fault localization

feature selection

statistical debugging