Feature-FL: Feature-Based Fault Localization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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 ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Feature-FL</monospace> ): A family fault localization methodology of feature-based metrics by combining the feature diversity from the view of program features into suspiciousness evaluation. Specifically, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Feature-FL</monospace> defines a concept of branching execution probability to abstract program behaviors as the values of features. Then, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Feature-FL</monospace> uses feature selection (i.e., a family of feature-based metrics) to evaluate the relevance of each feature with program failures. Finally, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Feature-FL</monospace> 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 <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Feature-FL</monospace> , and conduct an extensive study to evaluate the effectiveness of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Feature-FL</monospace> 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 <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Feature-FL</monospace> significantly outperforms the state-of-the-art spectrum-based formulas, e.g., an average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">saving</i> of at least 30% over spectrum-based formulas in case of real faults.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it