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KG4CraSolver: Recommending Crash Solutions via Knowledge Graph

Published: 30 November 2023 Publication History

Abstract

Fixing crashes is challenging, and developers often discuss their encountered crashes and refer to similar crashes and solutions on online Q&A forums (e.g., Stack Overflow). However, a crash often involves very complex contexts, which includes different contextual elements, e.g., purposes, environments, code, and crash traces. Existing crash solution recommendation or general solution recommendation techniques only use an incomplete context or treat the entire context as pure texts to search relevant solutions for a given crash, resulting in inaccurate recommendation results. In this work, we propose a novel crash solution knowledge graph (KG) to summarize the complete crash context and its solution with a graph-structured representation. To construct the crash solution KG automatically, we propose to leverage prompt learning to construct the KG from SO threads with a small set of labeled data. Based on the constructed KG, we further propose a novel KG-based crash solution recommendation technique KG4CraSolver, which precisely finds the relevant SO thread for an encountered crash by finely analyzing and matching the complete crash context based on the crash solution KG. The evaluation results show that the constructed KG is of high quality and KG4CraSolver outperforms baselines in terms of all metrics (e.g., 13.4%-113.4% MRR improvements). Moreover, we perform a user study and find that KG4CraSolver helps participants find crash solutions 34.4% faster and 63.3% more accurately.

Supplementary Material

Video (fse23main-p674-p-video.mp4)
"Fixing crashes is challenging, and developers often discuss their encountered crashes and refer to similar crashes and solutions on online Q&A forums (e.g., Stack Overflow). However, a crash often involves very complex contexts, which includes different contextual elements, e.g., purposes, environments, code, and crash traces. Existing crash solution recommendation or general solution recommendation techniques only use an incomplete context or treat the entire context as pure texts to search relevant solutions for a given crash, resulting in inaccurate recommendation results. In this work, we propose a novel crash solution knowledge graph (KG) to summarize the complete crash context and its solution with a graph-structured representation. To construct the crash solution KG automatically, we propose to leverage prompt-learning to construct the KG from SO threads with a small set of labeled data. Based on the constructed KG, we further propose a novel KG-based crash solution recommendation technique KG4CraSolver, which precisely finds the relevant SO thread for an encountered crash by finely analyzing and matching the complete crash context based on the crash solution KG. The evaluation results show that the constructed KG is of high quality and KG4CraSolver outperforms baselines in terms of all metrics (e.g., 13.4%-113.4% MRR improvements). Moreover, we perform a user study and find that KG4CraSolver helps participants find crash solutions 34.4% faster and 63.3% more accurately."

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  • (2024)A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault LocalizationProceedings of the ACM on Software Engineering10.1145/36607711:FSE(1424-1446)Online publication date: 12-Jul-2024

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cover image ACM Conferences
ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
November 2023
2215 pages
ISBN:9798400703270
DOI:10.1145/3611643
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  • (2024)A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault LocalizationProceedings of the ACM on Software Engineering10.1145/36607711:FSE(1424-1446)Online publication date: 12-Jul-2024

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