Bug tracking systems, which help to track the reported software bugs, have been widely used in software development and maintenance. In these systems, recognizing relevant source files among a large number of source files for a given bug report is a time-consuming and labor-intensive task for software developers. To tackle this problem, information retrieval methods have been widely used to capture either the textual similarities or the semantic similarities between bug reports and source files. However, these two types of similarities are usually considered separately and the historical bug fixings are largely ignored by the existing methods. In this paper, we propose a supervised topic modeling method (STMLOCATOR) for automatically locating the relevant source files for a given bug report. In particular, the proposed model is built upon three key observations. First, supervised modeling can effectively make use of the existing fixing histories. Second, certain words in bug reports tend to appear multiple times in their relevant source files. Third, longer source files tend to have more bugs. By integrating the above three observations, the proposed STMLOCATOR utilizes historical fixings in a supervised way and learns both the textual similarities and semantic similarities between bug reports and source files. We further consider a special type of bug reports with stack-traces in bug reports, and propose a variant of STMLOCATOR to tailor for such bug reports. Experimental evaluations on three real data sets demonstrate that the proposed STMLOCATOR can achieve up to 23.6% improvement in terms of prediction accuracy over its best competitors, and scales linearly with the size of the data. Moreover, the proposed variant further improves STMLOCATOR by up to 76.2% on those bug reports with stack-traces.
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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
- NSF-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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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
