Large Language Models (LLMs) are rapidly transforming software engineering workflows, especially in areas like bug report clustering and summarization. With their deep contextual understanding, these models are enabling teams to automate traditionally manual and time-consuming processes. Leveraging LLMs for managing bug reports can significantly enhance the productivity of engineering teams by improving prioritization, triage, and actionable insights extraction.
The Challenges in Bug Report Management
Modern software projects, especially at scale, generate a massive volume of bug reports from developers, testers, and end users. These reports often contain redundant or duplicate entries, vague language, and inconsistent formats. Challenges in managing bug reports include:
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Duplication: Multiple users may report the same bug using different terms.
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Volume: Manually sorting through hundreds or thousands of reports is time-intensive.
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Context loss: Critical context may be buried in verbose or poorly written descriptions.
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Prioritization: Determining the severity or urgency of each bug can be difficult without proper summarization.
These challenges can lead to delays in addressing critical issues, misallocation of resources, and increased technical debt.
LLMs for Clustering Bug Reports
Clustering involves grouping similar bug reports based on their semantic similarity. LLMs such as GPT-4, BERT, and their domain-specific derivatives (e.g., CodeBERT, BugLM) can be fine-tuned or used off-the-shelf to perform this task efficiently.
How It Works
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Embedding Generation: Each bug report is converted into a dense vector using an LLM. This vector captures semantic nuances beyond simple keyword matching.
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Clustering Algorithm: Techniques like K-means, HDBSCAN, or spectral clustering group vectors that are semantically similar.
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Duplicate Detection: Bug reports with very similar embeddings can be flagged as potential duplicates.
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Visualization & Review: Clustered reports can be reviewed using dimensionality reduction techniques like t-SNE or UMAP for visual inspection.
Benefits
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Improved triage by reducing duplication and organizing reports by topic.
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Automatic detection of recurring issues or regressions.
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Better resource allocation as teams can focus on unique, high-priority clusters.
Real-World Tools and Applications
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Microsoft’s DeepTriage uses embeddings from LLMs to automate bug report assignment and categorization.
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BugReport Assistant (by Facebook AI) clusters reports using transformer-based models trained on large-scale bug datasets.
LLMs for Bug Report Summarization
Summarization helps in distilling long and verbose bug reports into concise and informative descriptions. LLMs excel at this by understanding the core issues, reproduction steps, and impact, then generating readable and structured summaries.
Types of Summarization
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Extractive Summarization: Selecting the most important sentences from the bug report. Traditional but less flexible.
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Abstractive Summarization: Generating new text that captures the essence of the report, often more useful for developers and managers.
LLM Capabilities
LLMs like GPT-4, T5, and BART are capable of:
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Understanding nuanced technical language.
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Distilling multi-paragraph reports into one or two high-quality sentences.
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Generating summaries that highlight key metadata like stack traces, error codes, steps to reproduce, and affected modules.
Use Cases
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Dashboard Integration: Concise summaries on dashboards or issue trackers save engineers time.
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Email Digests: Automatically generated summaries for daily or weekly bug triage reports.
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Search Optimization: Enhanced indexing and retrieval based on summarized content.
Combining Clustering and Summarization
The real power of LLMs emerges when clustering and summarization are combined:
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Cluster Summarization: Generating a summary for an entire group of similar bug reports helps identify root causes and common themes.
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Topic Labeling: LLMs can assign meaningful titles to clusters, enhancing navigability.
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Insight Extraction: Aggregated information across reports may reveal trends, such as increased crashes post a recent release.
This hybrid approach allows for scalable, automated, and insightful bug management pipelines.
Practical Considerations
Model Selection
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Off-the-shelf models (e.g., GPT-4 via API): Quick to implement and often accurate.
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Fine-tuned models: Training LLMs on historical bug data improves domain performance.
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Open-source models: Options like CodeT5 or RoBERTa can be used in on-premise environments for privacy-sensitive use cases.
Preprocessing
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Normalize bug report format (remove HTML, anonymize user data).
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Segment sections (title, steps to reproduce, expected vs actual behavior).
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Tokenize long reports for LLMs with input size limitations.
Evaluation Metrics
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Clustering: Silhouette score, adjusted Rand index, and manual validation.
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Summarization: ROUGE, BLEU, and human-in-the-loop assessment for relevance and correctness.
Limitations
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LLMs may hallucinate or generate plausible but incorrect summaries.
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Bias and drift in language models may misinterpret domain-specific jargon.
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Latency and cost: API-based models can introduce performance or pricing challenges at scale.
Future Directions
As LLMs continue to evolve, their application in bug report clustering and summarization will become more seamless and intelligent. Emerging trends include:
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Multimodal bug triage: Incorporating logs, screenshots, and voice feedback.
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End-to-end automation: From bug detection (via telemetry) to summarization and ticket generation.
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Conversational interfaces: Engineers interacting with bug databases using natural language queries.
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Active learning: Using developer feedback to continuously improve clustering and summary quality.
Conclusion
Large Language Models are revolutionizing bug report management by enabling scalable clustering and summarization. These capabilities empower software teams to efficiently navigate large volumes of reports, reduce duplication, and accelerate issue resolution. While challenges remain around accuracy and integration, the benefits in speed, insight, and productivity make LLMs an invaluable asset for modern software development lifecycles.