[Paper] MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences

Source: arXiv - 2601.06789v1

Overview

MemGovern tackles a core blind spot of today’s autonomous software‑engineering (SWE) agents: they operate in a “closed‑world” and ignore the massive, publicly available knowledge base of human debugging experiences on platforms like GitHub. By turning raw issue‑tracking data into structured, searchable “experience cards,” MemGovern equips agents with a memory of real‑world fixes, boosting their problem‑solving success on benchmark tasks.

Key Contributions

• Experience Governance Pipeline – A systematic method for cleaning, normalizing, and enriching raw GitHub issue/PR data into a uniform “experience card” format that agents can consume directly.
• Agentic Experience Search – A logic‑driven retrieval strategy that lets an agent query the memory using its current reasoning state, rather than relying on simple keyword matching.
• Large‑Scale Memory Construction – Generation of ~135 K governed experience cards covering diverse languages, libraries, and bug categories.
• Plug‑in Architecture – MemGovern can be attached to existing code‑generation or debugging agents without retraining the underlying model.
• Empirical Gains – Integration with a state‑of‑the‑art SWE agent raises the SWE‑bench Verified resolution rate by 4.65 %, a notable jump in a tightly competitive benchmark.

Methodology

1. Data Harvesting – Pull issue, pull‑request, and discussion threads from a curated list of popular GitHub repositories.
2. Governance & Normalization – Apply a series of heuristics and lightweight NLP models to (a) strip noise (e.g., boilerplate text, logs), (b) identify the root cause, (c) extract the concrete fix (code diff or command), and (d) tag the card with metadata such as language, library, and error type.
3. Experience Card Creation – Each card stores a concise description, the actionable fix, and structured tags, forming a self‑contained knowledge unit.
4. Agentic Search Engine – When an agent encounters a bug, it first generates a logical query (e.g., “NullPointerException in Java Stream API”). The search engine matches this query against the tags and semantic embeddings of the cards, returning the most relevant experiences.
5. Memory‑Augmented Reasoning – The agent incorporates the retrieved cards into its chain‑of‑thought prompting, allowing it to adapt the human‑derived fix to the current codebase.

Results & Findings

• Resolution Rate Boost – On the SWE‑bench Verified suite, the baseline agent solved X % of tasks; with MemGovern, the success rate rose by 4.65 % (absolute).
• Recall of Rare Bugs – The memory helped the agent handle low‑frequency error patterns (e.g., obscure library version conflicts) that were previously missed.
• Low Overhead – Adding MemGovern increased inference latency by only ~0.3 s per query, thanks to efficient indexing of the experience cards.
• Generalizability – Experiments across Python, JavaScript, and Java projects showed consistent improvements, indicating the approach is language‑agnostic.

Practical Implications

• Faster Debugging Assistants – Developers can plug MemGovern into existing AI pair‑programmers (e.g., GitHub Copilot, Tabnine) to get context‑rich suggestions that reflect real‑world fixes rather than generic patterns.
• Reduced Model Training Costs – Since the memory is a separate, updatable knowledge base, teams can keep the agent’s core model static while continuously enriching the experience cards with new open‑source data.
• Compliance & Auditing – Each card retains provenance (repo, issue URL, timestamp), making it easier for enterprises to trace where a suggested fix originated—a boon for security reviews.
• On‑Premise Knowledge Bases – Companies can run a private MemGovern instance seeded with internal ticketing systems (Jira, Azure DevOps), giving agents access to proprietary debugging experience without exposing code.
• Improved CI/CD Automation – Automated code‑review bots can query the memory to propose patches for failing builds, cutting down mean‑time‑to‑repair (MTTR).

Limitations & Future Work

• Noise in Source Data – Despite governance steps, some cards still contain ambiguous or incomplete fixes, which can mislead the agent.
• Scalability of Governance – The current pipeline relies on heuristic rules; scaling to millions of repositories may require more robust, possibly supervised, extraction models.
• Domain Specificity – Highly specialized domains (e.g., embedded systems) have sparse open‑source issue data, limiting the memory’s coverage.
• Future Directions – The authors plan to (1) integrate active learning where agents flag low‑quality cards for human review, (2) explore multimodal cards that include logs or screenshots, and (3) evaluate long‑term maintenance strategies for keeping the memory up‑to‑date with evolving libraries.

Authors

• Qihao Wang
• Ziming Cheng
• Shuo Zhang
• Fan Liu
• Rui Xu
• Heng Lian
• Kunyi Wang
• Xiaoming Yu
• Jianghao Yin
• Sen Hu
• Yue Hu
• Shaolei Zhang
• Yanbing Liu
• Ronghao Chen
• Huacan Wang

Paper Information

• arXiv ID: 2601.06789v1
• Categories: cs.SE, cs.AI
• Published: January 11, 2026
• PDF: Download PDF