The impact of bug localization based on crash report mining: A developers' perspective

• Main Authors: MEDEIROS, Marcos, KULESZA, Uirá, COELHO, Roberta, BONIFACIO, Rodrigo, TREUDE, Christoph, BARBOSA, Eiji Adachi
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Software crash; Bug correlation; Bug localization; Crash reports; Stack traces; Bug prioritization; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8917; https://ink.library.smu.edu.sg/context/sis_research/article/9920/viewcontent/babalu.pdf
• Institution: Singapore Management University

Developers often use crash reports to understand the root cause of bugs. However, locating the buggy source code snippet from such information is a challenging task, mainly when the log database contains many crash reports. To mitigate this issue, recent research has proposed and evaluated approaches for grouping crash report data and using stack trace information to locate bugs. The effectiveness of such approaches has been evaluated by mainly comparing the candidate buggy code snippets with the actual changed code in bug-fix commits—which happens in the context of retrospective repository mining studies. Therefore, the existing literature still lacks discussing the use of such approaches in the daily life of a software company, which could explain the developers’ perceptions on the use of these approaches. In this paper, we report our experience of using an approach for grouping crash reports and finding buggy code on a weekly basis for 18 months, within three development teams in a software company. We grouped over 750,000 crash reports, opened over 130 issues, and collected feedback from 18 developers and team leaders. Among other results, we observe that the amount of system logs related to a crash report group is not the only criteria developers use to choose a candidate bug to be analyzed. Instead, other factors were considered, such as the need to deliver customer-prioritized features and the difficulty of solving complex crash reports (e.g., architectural debts), to cite some. The approach investigated in this study correctly suggested the buggy file most of the time—the approach’s precision was around 80%. In this study, the developers also shared their perspectives on the usefulness of the suspicious files and methods extracted from crash reports to fix related bugs.