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

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

TL;DR

DepRadar tackles the challenge of determining whether defects fixed in DL libraries affect downstream client programs by coordinating four specialized agents to extract defect semantics, synthesize client-facing defect patterns, and verify impact on code. It combines static analysis, domain-informed reasoning, and progressive context augmentation to map low-level patch changes to user-facing configurations and API usage. On Transformers and Megatron, it achieves high defect-pattern precision (up to 95%) and robust downstream impact detection (F1 up to 85%), outperforming baselines and validating real-world impacted clients. The framework enables practical defect awareness for dependency updates, reducing silent regressions and guiding targeted mitigations in production DL pipelines.

Abstract

Deep learning libraries like Transformers and Megatron are now widely adopted in modern AI programs. However, when these libraries introduce defects, ranging from silent computation errors to subtle performance regressions, it is often challenging for downstream users to assess whether their own programs are affected. Such impact analysis requires not only understanding the defect semantics but also checking whether the client code satisfies complex triggering conditions involving configuration flags, runtime environments, and indirect API usage. We present DepRadar, an agent coordination framework for fine grained defect and impact analysis in DL library updates. DepRadar coordinates four specialized agents across three steps: 1. the PR Miner and Code Diff Analyzer extract structured defect semantics from commits or pull requests, 2. the Orchestrator Agent synthesizes these signals into a unified defect pattern with trigger conditions, and 3. the Impact Analyzer checks downstream programs to determine whether the defect can be triggered. To improve accuracy and explainability, DepRadar integrates static analysis with DL-specific domain rules for defect reasoning and client side tracing. We evaluate DepRadar on 157 PRs and 70 commits across two representative DL libraries. It achieves 90% precision in defect identification and generates high quality structured fields (average field score 1.6). On 122 client programs, DepRadar identifies affected cases with 90% recall and 80% precision, substantially outperforming other baselines.

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

TL;DR

DepRadar tackles the challenge of determining whether defects fixed in DL libraries affect downstream client programs by coordinating four specialized agents to extract defect semantics, synthesize client-facing defect patterns, and verify impact on code. It combines static analysis, domain-informed reasoning, and progressive context augmentation to map low-level patch changes to user-facing configurations and API usage. On Transformers and Megatron, it achieves high defect-pattern precision (up to 95%) and robust downstream impact detection (F1 up to 85%), outperforming baselines and validating real-world impacted clients. The framework enables practical defect awareness for dependency updates, reducing silent regressions and guiding targeted mitigations in production DL pipelines.

Abstract

Deep learning libraries like Transformers and Megatron are now widely adopted in modern AI programs. However, when these libraries introduce defects, ranging from silent computation errors to subtle performance regressions, it is often challenging for downstream users to assess whether their own programs are affected. Such impact analysis requires not only understanding the defect semantics but also checking whether the client code satisfies complex triggering conditions involving configuration flags, runtime environments, and indirect API usage. We present DepRadar, an agent coordination framework for fine grained defect and impact analysis in DL library updates. DepRadar coordinates four specialized agents across three steps: 1. the PR Miner and Code Diff Analyzer extract structured defect semantics from commits or pull requests, 2. the Orchestrator Agent synthesizes these signals into a unified defect pattern with trigger conditions, and 3. the Impact Analyzer checks downstream programs to determine whether the defect can be triggered. To improve accuracy and explainability, DepRadar integrates static analysis with DL-specific domain rules for defect reasoning and client side tracing. We evaluate DepRadar on 157 PRs and 70 commits across two representative DL libraries. It achieves 90% precision in defect identification and generates high quality structured fields (average field score 1.6). On 122 client programs, DepRadar identifies affected cases with 90% recall and 80% precision, substantially outperforming other baselines.
Paper Structure (26 sections, 7 figures, 10 tables)

This paper contains 26 sections, 7 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Motivation Example
  3. Approach
  4. Defect Semantics Extraction
  5. Commit/PR Miner Agent
  6. Code Diff Analyzer Agent
  7. Defect Pattern Generation
  8. Client-Side Defect Impact Analysis
  9. Version-Aware File Matching.
  10. Contextual Searching via Syntax-Aware Extraction.
  11. Post-processing and Verification.
  12. Impact Report Generation.
  13. Evaluation
  14. Experimental Settings
  15. RQ1: Can DepRadar effectively generate defect patterns?
  16. ...and 11 more sections

Figures (7)

  • Figure 1: Motivation Example.
  • Figure 2: Overview of our approach.
  • Figure 3: Prompt design for the Miner Agent: mining defect patterns from unstructured PR metadata for PR #38811 and associated PR #38105.
  • Figure 4: Prompt design for the Code Diff Agent: summarizing patch semantics for PR #38491.
  • Figure 5: Defect Pattern example synthesized for PR #37575.
  • ...and 2 more figures