XAI for Coding Agent Failures: Transforming Raw Execution Traces into Actionable Insights
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908869996838912 |
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| author | Joshi, Arun |
| author_facet | Joshi, Arun |
| contents | Large Language Model (LLM)-based coding agents show promise in automating software development tasks, yet they frequently fail in ways that are difficult for developers to understand and debug. While general-purpose LLMs like GPT can provide ad-hoc explanations of failures, raw execution traces remain challenging to interpret even for experienced developers. We present a systematic explainable AI (XAI) approach that transforms raw agent execution traces into structured, human-interpretable explanations. Our method consists of three key components: (1) a domain-specific failure taxonomy derived from analyzing real agent failures, (2) an automatic annotation system that classifies failures using defined annotation schema, (3) a hybrid explanation generator that produces visual execution flows, natural language explanations, and actionable recommendations. Through a user study with 20 participants (10 technical, 10 non-technical), we demonstrate that our approach enables users to identify failure root causes 2.8 times faster and propose correct fixes with 73% higher accuracy compared to raw execution traces. Importantly, our structured approach outperforms ad-hoc state of the art models explanations by providing consistent, domain-specific insights with integrated visualizations. Our work establishes a framework for systematic agent failure analysis, addressing the critical need for interpretable AI systems in software development workflows |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_05941 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | XAI for Coding Agent Failures: Transforming Raw Execution Traces into Actionable Insights Joshi, Arun Software Engineering Artificial Intelligence I.2.6 Large Language Model (LLM)-based coding agents show promise in automating software development tasks, yet they frequently fail in ways that are difficult for developers to understand and debug. While general-purpose LLMs like GPT can provide ad-hoc explanations of failures, raw execution traces remain challenging to interpret even for experienced developers. We present a systematic explainable AI (XAI) approach that transforms raw agent execution traces into structured, human-interpretable explanations. Our method consists of three key components: (1) a domain-specific failure taxonomy derived from analyzing real agent failures, (2) an automatic annotation system that classifies failures using defined annotation schema, (3) a hybrid explanation generator that produces visual execution flows, natural language explanations, and actionable recommendations. Through a user study with 20 participants (10 technical, 10 non-technical), we demonstrate that our approach enables users to identify failure root causes 2.8 times faster and propose correct fixes with 73% higher accuracy compared to raw execution traces. Importantly, our structured approach outperforms ad-hoc state of the art models explanations by providing consistent, domain-specific insights with integrated visualizations. Our work establishes a framework for systematic agent failure analysis, addressing the critical need for interpretable AI systems in software development workflows |
| title | XAI for Coding Agent Failures: Transforming Raw Execution Traces into Actionable Insights |
| topic | Software Engineering Artificial Intelligence I.2.6 |
| url | https://arxiv.org/abs/2603.05941 |