Source-linked AI summary
Deep Learning-based Bug Triage System
Sourabh Pal
TL;DR
Bug triage requires substantial effort to categorize and assign software defects. The paper presents an automated RoBERTa-based system for component identification and bug classification, achieving 0.90 identification accuracy within five training epochs. The authors propose extending evaluation to larger, more heterogeneous datasets and improving runtime efficiency.
Problem
Bug analysis and triage consume substantial software-maintenance effort, motivating automated identification of buggy components and actual bugs.
Method
The system uses a fine-tuned RoBERTa-base transformer with contextual representations to identify affected components and classify bugs.
Results
0.90 identification accuracy was achieved within five training epochs.
Takeaways & Limitations
Transformer-based architectures can reduce manual defect-classification overhead while maintaining high precision.
Takeaways & Limitations
Future evaluation is planned on larger and more heterogeneous multi-project datasets, with severity prediction and lightweight models also left for future work.
Abstract
from arXiv · showhide
Effective bug triage is crucial for streamlining the software development lifecycle by accurately categorizing and assigning reported software defects. In this paper, we propose an automated bug triage system built upon the pre-trained RoBERTa-base transformer architecture. By leveraging deep contextual representations, our approach efficiently classifies incoming bug reports to optimize assignment. Experimental evaluation demonstrates that the proposed system achieves a strong bug identification accuracy of 0.90 within just five training epochs. These findings highlight the efficiency and high performance of fine-tuned transformer models for practical software engineering automation.
1 Introduction
Software maintenance is costly and bug analysis consumes substantial developer time, motivating automated triage that identifies buggy components and filters non-buggy reports.
- Software maintenance consumes 40-80% of software production costs, while bug analysis takes over 30% of maintenance time.
- Bug reports are stored in issue-tracking systems, where identifying the responsible component helps developers locate bug-producing code.
- Automated component identification can reduce the time and resources required for manual bug triage.
- The system identifies whether incoming reports are actual bugs because reports may be duplicates, invalid, or already resolved.
2 Dataset Description
The study uses Bugzilla client-software reports spanning sixteen products and extracts report fields, including summaries, products, components, and resolutions, with resolution statuses defining bug outcomes.
- The extracted dataset includes BugSummary, Product, Component, and Resolution fields for analysis.
- Resolution records the current bug status, including FIXED, INVALID, WONTFIX, INACTIVE, DUPLICATE, WORKSFORME, INCOMPLETE, EXPIRED, and MOVED.
- The statuses distinguish fixed issues from invalid, duplicate, unresolved, insufficiently documented, expired, or misrouted reports.
- Bugzilla provided 69431 bug reports from the Client Software category, covering sixteen products.
3 Architecture Overview
The proposed architecture automatically identifies affected components and classifies bugs through data collection, cleaning, and classification using NLP preprocessing and contextual deep learning.
- The architecture automates affected-component identification and bug classification in three steps: data collection, data cleaning, and classification.
- Figure 1 schematically represents the proposed approach.
- BugSummary, Component, and Resolution fields are extracted from Bugzilla and stored for analysis.
- Bug summaries are preprocessed using special-character removal, stop-word elimination, and lemmatization.
- The classification approach uses attention-based contextual features from bug-resolution summaries to improve classifier learning.
4 Experimental Setup
The experiments evaluate two bug-triage tasks with RoBERTa using Bug Summary text, while components and resolutions provide the respective labels.
- The experiments address component identification and binary classification of whether a newly assigned bug is buggy or non-buggy.
- Bug Summary supplies the model input, Component labels the component task, and Resolution labels bugginess.
- Experiments were conducted on a single Tesla T4 GPU.
- A bug is labeled 1 when its resolution is FIXED and 0 otherwise.
- RoBERTa is trained using a random 90% training split and 10% test split.
5 Results and Analysis
The RoBERTa-base model was evaluated for component and bug classification using five training epochs. Component accuracy reached about 0.7, while bug identification exceeded 0.9 but showed signs of overfitting.
- Experimental Setup: The experiments used a RoBERTa-base transformer and analyzed training and validation loss alongside accuracy over five epochs.The evaluation covered both component classification and buggy-versus-non-buggy classification.
- Component Identification: About 0.7 component-identification accuracy was achieved after five epochs, with decreasing training and validation losses across epochs.The task involved multiclass classification across 325 unique components.
- Bug Identification: Over 0.9 bug-identification accuracy was achieved after five epochs, although increasing validation loss indicated slight overfitting from noisy data.The model’s training loss decreased while validation loss increased during bug classification.
6 Conclusion
The paper presents an automated bug-triage system based on fine-tuned RoBERTa-base contextual representations. It reports 0.90 identification accuracy within five epochs and proposes broader evaluation and efficiency improvements as future work.
- Conclusion: The proposed system automates bug categorization and assignment to improve bug-triage efficiency in software development.Its stated purpose is to reduce manual overhead associated with defect classification.
- Conclusion: The fine-tuned RoBERTa-base system achieved 0.90 identification accuracy within five training epochs.The system captures deep contextual semantics from bug reports.
- Future Work: Future work will evaluate larger heterogeneous multi-project datasets, add severity prediction, and explore distilled transformers for lower runtime and deployment costs.These extensions target broader evaluation and improved efficiency.