[Paper] DepRadar: Agentic Coordination for Context Aware Defect Impact Analysis in Deep Learning Libraries

Source: arXiv - 2601.09440v1

Overview

Deep learning (DL) libraries such as Transformers and Megatron power countless AI applications, but even tiny defects in these libraries can silently break downstream projects. The paper introduces DepRadar, a framework that automatically extracts defect semantics from library changes and determines whether a given user program could be impacted, all while keeping the analysis explainable for developers.

Key Contributions

• Agent‑based coordination: Four specialized agents (PR Miner, Code Diff Analyzer, Orchestrator, Impact Analyzer) work together to turn raw code changes into actionable defect patterns.
• Structured defect semantics: Generates a unified, machine‑readable description of a defect, including trigger conditions like config flags, runtime environment, and indirect API usage.
• Hybrid analysis engine: Combines static code analysis with DL‑specific domain rules to reason about defect propagation and client‑side tracing.
• Empirical validation: Evaluated on 157 pull requests and 70 commits from two major DL libraries, achieving 90 % precision in defect identification and 90 % recall / 80 % precision in impact detection on 122 downstream programs.
• Explainability: Produces human‑readable “defect fields” (average field score = 1.6) that help developers understand why a client program is flagged.

Methodology

1. PR Miner & Code Diff Analyzer – These agents scrape pull‑request metadata and parse the code diffs to capture what changed (e.g., modified functions, added flags).
2. Orchestrator Agent – Merges the raw signals into a defect pattern: a structured object that lists the altered API, the conditions under which the bug manifests (e.g., use_fp16=True && CUDA>=11.2), and the expected symptom (silent error, performance drop, etc.).
3. Impact Analyzer – Takes a downstream program, runs a lightweight static analysis enriched with the defect pattern, and checks whether the program’s configuration and call graph satisfy the trigger conditions. If a match is found, the tool reports a potential impact and highlights the exact code locations involved.

The agents communicate through a simple JSON schema, making the pipeline extensible to other libraries or languages.

Results & Findings

• Defect identification: 90 % precision, meaning false positives in recognizing a library change as a defect were rare.
• Field quality: Structured defect fields scored an average of 1.6 on a 0–2 scale (higher is better), indicating that most generated fields were both complete and accurate.
• Impact detection: On 122 real client programs, DepRadar recalled 90 % of the truly affected cases while maintaining 80 % precision, outperforming baseline static‑analysis tools by a large margin (baseline recall ≈ 55 %).
• Explainability: Developers reported that the generated trigger conditions helped them quickly verify whether a fix was needed, reducing the time spent on manual debugging.

Practical Implications

• Faster patch triage: Library maintainers can automatically flag downstream projects that need urgent updates after a defect is merged, cutting down on post‑release breakages.
• CI/CD integration: DepRadar’s agents can be hooked into continuous‑integration pipelines to warn developers when they pull in a new library version that may affect their code.
• Risk assessment for upgrades: Teams can run the Impact Analyzer before upgrading a DL library, obtaining a clear impact report rather than relying on vague release notes.
• Cross‑project safety nets: Open‑source ecosystems (e.g., Hugging Face Transformers) can publish structured defect patterns alongside releases, enabling downstream users to consume them automatically.

Overall, DepRadar turns a traditionally manual, error‑prone process into a repeatable, data‑driven workflow that aligns with modern DevOps practices.

Limitations & Future Work

• Static‑analysis focus: The current implementation may miss defects that only surface at runtime under specific data distributions; integrating dynamic profiling could improve coverage.
• Domain rule maintenance: The DL‑specific rule set requires periodic updates as libraries evolve, which could become a maintenance burden.
• Scalability to larger ecosystems: While evaluated on two libraries, scaling the agent framework to dozens of libraries with heterogeneous build systems may need additional engineering.
• User feedback loop: Future work could incorporate developer feedback to refine defect patterns automatically, creating a semi‑supervised learning loop.

Despite these constraints, DepRadar demonstrates a promising direction for making deep‑learning library updates safer and more transparent for the broader developer community.

Authors

• Yi Gao
• Xing Hu
• Tongtong Xu
• Jiali Zhao
• Xiaohu Yang
• Xin Xia

Paper Information

• arXiv ID: 2601.09440v1
• Categories: cs.SE
• Published: January 14, 2026
• PDF: Download PDF