Automatic solution summarization for crash bugs

• Main Authors: WANG, Haoye, XIA, Xin, LO, David, GRUNDY, John C., WANG, Xinyu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Java; Computer bugs; Diversity reception; Documentation; Search engines; Software; Software engineering; Databases and Information Systems; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6712; https://ink.library.smu.edu.sg/context/sis_research/article/7715/viewcontent/icse214.pdf
• Institution: Singapore Management University

The causes of software crashes can be hidden anywhere in the source code and development environment. When encountering software crashes, recurring bugs that are discussed on Q&A sites could provide developers with solutions to their crashing problems. However, it is difficult for developers to accurately search for relevant content on search engines, and developers have to spend a lot of manual effort to find the right solution from the returned results. In this paper, we present CRASOLVER, an approach that takes into account both the structural information of crash traces and the knowledge of crash-causing bugs to automatically summarize solutions from crash traces. Given a crash trace, CRASOLVER retrieves relevant questions from Q&A sites by combining a proposed position dependent similarity – based on the structural information of the crash trace – with an extra knowledge similarity, based on the knowledge from official documentation sites. After obtaining the answers to these questions from the Q&A site, CRASOLVER summarizes the final solution based on a multi-factor scoring mechanism. To evaluate our approach, we built two repositories of Java and Android exception-related questions from Stack Overflow with size of 69,478 and 33,566 questions respectively. Our user study results using 50 selected Java crash traces and 50 selected Android crash traces show that our approach significantly outperforms four baselines in terms of relevance, usefulness, and diversity. The evaluation also confirms the effectiveness of the relevant question retrieval component in our approach for crash traces.