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Related Experiment Video

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Modeling Fetal Alcohol Spectrum Disorders in Zebrafish to Characterize the Impact of an Adverse Embryonic Environment on Adult Social Behavior

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On Refining the SZZ Algorithm with Bug Discussion Data.

Pooja Rani¹, Fernando Petrulio¹, Alberto Bacchelli¹

¹Department of Informatics, University of Zurich, Zurich, Switzerland.

Empirical Software Engineering

|July 29, 2024

Summary

This summary is machine-generated.

Incorporating bug discussion details significantly improves the accuracy of the SZZ algorithm in identifying bug-introducing commits. This enhancement aids in pinpointing software defects more precisely by analyzing related files mentioned in developer conversations.

Keywords:

Bug-introducing commits Empirical research Mozilla Pull request SZZ algorithm Software quality Taxonomy

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Area of Science:

Software Engineering
Empirical Software Engineering
Defect Analysis

Background:

Software quality research often relies on historical defect data.
The SZZ algorithm is a prevailing technique for identifying bug-introducing commits based on code modifications.
Existing SZZ variants struggle with accuracy due to issues like tangled and ghost commits.

Purpose of the Study:

To investigate if bug discussion content can improve the accuracy of the SZZ algorithm.
To identify related and external files from bug discussions to enhance SZZ efficacy.
To address limitations of current SZZ methods in pinpointing defect origins.

Main Methods:

Leveraged manually linked bug reports from Mozilla developers.
Created the RoTEB dataset of 12,472 bug reports.
Manually inspected a sample of bug reports to assess file relevance for SZZ.
Augmented the SZZ algorithm with information from bug discussions and evaluated its performance.

Main Results:

Defined a taxonomy for developer references to files in bug discussions.
Observed that bug discussions frequently mention files beneficial for SZZ.
Validated that integrating file references from discussions improves SZZ precision in pinpointing bug-introducing commits.
Found no significant impact on SZZ recall.

Conclusions:

Bug discussions offer valuable information for enhancing SZZ algorithm precision.
The RoTEB dataset provides a resource for future research on defect analysis.
Further exploration is needed to address tangled and ghost commits effectively.