[Paper] From Issues to Insights: RAG-based Explanation Generation from Software Engineering Artifacts
Source: arXiv - 2601.05721v1
Overview
Modern software systems are getting so intricate that developers and users alike struggle to understand why a system behaves the way it does. The paper “From Issues to Insights: RAG‑based Explanation Generation from Software Engineering Artifacts” shows that the wealth of information stored in issue‑tracking systems (e.g., GitHub Issues) can be turned into clear, context‑specific explanations using a Retrieval‑Augmented Generation (RAG) pipeline. The authors build a prototype that automatically drafts human‑readable explanations and demonstrate that it aligns with manually written ones 90 % of the time.
Key Contributions
- First RAG application for software‑engineering explanations – leverages issue‑tracker data instead of source code alone.
- Open‑source proof‑of‑concept built on publicly available LLMs and retrieval tools, enabling reproducibility.
- High alignment with human explanations (≈ 90 % match) while maintaining strong faithfulness to the original issue content.
- Comprehensive evaluation metrics covering alignment, faithfulness, and instruction adherence, showing the approach is both accurate and reliable.
- Blueprint for extending explainability beyond ML models to any system that logs development knowledge in structured artifacts.
Methodology
- Data Collection – The authors harvested a representative set of GitHub issues from an open‑source project, extracting titles, descriptions, comments, labels, and linked pull requests.
- Retrieval Layer – Using a dense vector store (e.g., FAISS) they indexed the issue texts. When a user asks for an explanation of a particular behavior, the system first retrieves the most relevant issue entries based on semantic similarity.
- Augmented Generation – The retrieved snippets are fed, together with a prompt that instructs the language model to “explain the observed behavior in plain language,” to a generative LLM (e.g., Llama‑2 or GPT‑3.5).
- Post‑processing & Validation – The generated text is filtered for consistency, checked against the source issue for factual grounding, and finally presented to the user.
- Evaluation – Human annotators compared the system’s output to manually written explanations, scoring alignment, factual faithfulness, and adherence to the instruction prompt.
The pipeline is deliberately modular, so each component (retriever, vector store, LLM) can be swapped out for newer or domain‑specific alternatives.
Results & Findings
| Metric | Outcome |
|---|---|
| Alignment with human explanations | ≈ 90 % of generated explanations were judged equivalent or near‑equivalent to the human baseline. |
| Faithfulness | Over 95 % of factual statements in the output were directly traceable to the retrieved issue content. |
| Instruction adherence | The LLM followed the “explain in plain language” prompt in > 93 % of cases, avoiding jargon or hallucinations. |
| Speed | End‑to‑end latency averaged 1.2 seconds per request on commodity hardware, making interactive use feasible. |
These numbers indicate that a RAG‑driven system can reliably turn raw issue data into developer‑friendly explanations without sacrificing accuracy.
Practical Implications
- On‑the‑fly documentation – Teams can generate up‑to‑date explanations for new features or bugs directly from their issue tracker, reducing the maintenance burden of traditional docs.
- Improved onboarding – New hires can query the system (“Why does component X fail under condition Y?”) and receive concise, context‑aware answers, accelerating ramp‑up.
- Support & troubleshooting – Customer‑facing support tools can surface automatically generated explanations, cutting down on manual knowledge‑base updates.
- Compliance & audit trails – Regulators often require clear rationales for system behavior; a RAG‑based explanation engine can produce auditable narratives anchored in recorded issues.
- Extensible to other artifacts – The same architecture can ingest commit messages, design docs, or test reports, broadening the scope of explainability across the software lifecycle.
Limitations & Future Work
- Dependence on issue quality – The approach assumes that issues are well‑written and contain the necessary technical details; noisy or sparse issue data can degrade output quality.
- Domain specificity – The prototype was evaluated on a single open‑source project; broader validation across varied languages, frameworks, and enterprise settings is needed.
- Scalability of retrieval – While FAISS works for modest corpora, massive industrial issue trackers may require more sophisticated indexing or hierarchical retrieval.
- Explainability of the explainer – The system itself is a black‑box LLM; future work could integrate self‑explanations or confidence scores to further increase trust.
- User interaction design – Exploring UI/UX patterns (e.g., interactive refinement of queries) could make the tool more usable in real development workflows.
Overall, the paper opens a promising path toward making the tacit knowledge embedded in issue trackers instantly accessible, turning “issues” into actionable insights for developers and organizations alike.
Authors
- Daniel Pöttgen
- Mersedeh Sadeghi
- Max Unterbusch
- Andreas Vogelsang
Paper Information
- arXiv ID: 2601.05721v1
- Categories: cs.SE
- Published: January 9, 2026
- PDF: Download PDF