Hybrid Risk-Aware Retrieval-Augmented Engineering Intelligence System for Automated Bug Analysis
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| Natura: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901156983209984 |
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| author | dhruv, sharma |
| author_facet | dhruv, sharma |
| contents | <p>Software systems generate vast volumes of logs and operational data, making debugging increasingly complex and time-consuming. Developers and QA engineers often rely on manual inspection to identify failures, which is inefficient, error-prone, and difficult to scale in modern distributed systems.</p> <p>Recent advancements in <span><span>Large Language Models</span></span> (LLMs) have significantly improved capabilities in reasoning and code understanding. However, these models lack access to structured engineering data such as system logs, historical bug reports, and runtime signals, limiting their effectiveness in real-world debugging scenarios.</p> <p>To address this limitation, <span><span>Retrieval-Augmented Generation</span></span> (RAG) has been introduced to enhance LLM performance by incorporating external knowledge retrieval. While RAG improves contextual reasoning, it does not account for system-level risk signals that are critical for identifying and prioritizing high-impact failures.</p> <p>In this work, we propose a <strong>Hybrid Risk-Aware Retrieval-Augmented Engineering Intelligence System</strong>. The proposed system integrates machine learning-based risk prediction into the retrieval pipeline, enabling prioritization of high-risk failure patterns during the reasoning process. By incorporating system-level signals, the approach dynamically enhances the relevance and impact of retrieved context.</p> <p>Unlike traditional RAG-based debugging systems, our method introduces a <strong>risk-aware retrieval mechanism</strong> that prioritizes failure-critical information, resulting in more accurate, efficient, and actionable debugging insights.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19338786 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Hybrid Risk-Aware Retrieval-Augmented Engineering Intelligence System for Automated Bug Analysis dhruv, sharma <p>Software systems generate vast volumes of logs and operational data, making debugging increasingly complex and time-consuming. Developers and QA engineers often rely on manual inspection to identify failures, which is inefficient, error-prone, and difficult to scale in modern distributed systems.</p> <p>Recent advancements in <span><span>Large Language Models</span></span> (LLMs) have significantly improved capabilities in reasoning and code understanding. However, these models lack access to structured engineering data such as system logs, historical bug reports, and runtime signals, limiting their effectiveness in real-world debugging scenarios.</p> <p>To address this limitation, <span><span>Retrieval-Augmented Generation</span></span> (RAG) has been introduced to enhance LLM performance by incorporating external knowledge retrieval. While RAG improves contextual reasoning, it does not account for system-level risk signals that are critical for identifying and prioritizing high-impact failures.</p> <p>In this work, we propose a <strong>Hybrid Risk-Aware Retrieval-Augmented Engineering Intelligence System</strong>. The proposed system integrates machine learning-based risk prediction into the retrieval pipeline, enabling prioritization of high-risk failure patterns during the reasoning process. By incorporating system-level signals, the approach dynamically enhances the relevance and impact of retrieved context.</p> <p>Unlike traditional RAG-based debugging systems, our method introduces a <strong>risk-aware retrieval mechanism</strong> that prioritizes failure-critical information, resulting in more accurate, efficient, and actionable debugging insights.</p> |
| title | Hybrid Risk-Aware Retrieval-Augmented Engineering Intelligence System for Automated Bug Analysis |
| url | https://doi.org/10.5281/zenodo.19338786 |