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One of the most important tasks related to managing bug reports is localizing the fault so that a fix can be applied. As such, prior work has aimed to automate this task of bug localization by formulating it as an information retrieval problem, where potentially buggy files are retrieved and ranked according to their textual similarity with a given bug report. However, there is often a notable semantic gap between the information contained in bug reports and identifiers or natural language contained within source code files. For user-facing software, there is currently a key source of information that could aid in bug localization, but has not been thoroughly investigated - information from the GUI. We investigate the hypothesis that, for end user-facing applications, connecting information in a bug report with information from the GUI, and using this to aid in retrieving potentially buggy files, can improve upon existing techniques for bug localization. To examine this phenomenon, we conduct a comprehensive empirical study that augments four baseline techniques for bug localization with GUI interaction information from a reproduction scenario to (i) filter out potentially irrelevant files, (ii) boost potentially relevant files, and (iii) reformulate text-retrieval queries. To carry out our study, we source the current largest dataset of fully-localized and reproducible real bugs for Android apps, with corresponding bug reports, consisting of 80 bug reports from 39 popular open-source apps. Our results illustrate that augmenting traditional techniques with GUI information leads to a marked increase in effectiveness across multiple metrics, including a relative increase in Hits@10 of 13-18%. Additionally, through further analysis, we find that our studied augmentations largely complement existing techniques.
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Automatically Selecting Follow-up Questions for Deficient Bug Reports
The availability of quality information in bug reports that are created daily by software users is key to rapidly fixing software faults. Improving incomplete or deficient bug reports, which are numerous in many popular and actively developed open source software projects, can make software maintenance more effective and improve software quality. In this paper, we propose a system that addresses the problem of bug report incompleteness by automatically posing follow-up questions, intended to elicit answers that add value and provide missing information to a bug report. Our system is based on selecting follow-up questions from a large corpus of already posted follow-up questions on GitHub. To estimate the best follow-up question for a specific deficient bug report we combine two metrics based on: 1) the compatibility of a follow-up question to a specific bug report; and 2) the utility the expected answer to the follow-up question would provide to the deficient bug report. Evaluation of our system, based on a manually annotated held-out data set, indicates improved performance over a set of simple and ablation baselines. A survey of software developers confirms the held-out set evaluation result that about half of the selected follow-up questions are considered valid. The survey also indicates that the valid follow-up questions are useful and can provide new information to a bug report most of the time, and are specific to a bug report some of the time.
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- Award ID(s):
- 1813253
- PAR ID:
- 10287699
- Date Published:
- Journal Name:
- IEEE International Working Conference on Mining Software Repositories
- ISSN:
- 2160-1852
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
