Fuzzy set-based automatic bug triaging (NIER track)

Abstract

Assigning a bug to the right developer is a key in reducing the cost, time, and efforts for developers in a bug fixing process. This assignment process is often referred to as bug triaging. In this paper, we propose Bugzie, a novel approach for automatic bug triaging based on fuzzy set-based modeling of bug-fixing expertise of developers. Bugzie considers a system to have multiple technical aspects, each is associated with technical terms. Then, it uses a fuzzy set to represent the developers who are capable/competent of fixing the bugs relevant to each term. The membership function of a developer in a fuzzy set is calculated via the terms extracted from the bug reports that (s)he has fixed, and the function is updated as new fixed reports are available. For a new bug report, its terms are extracted and corresponding fuzzy sets are union'ed. Potential fixers will be recommended based on their membership scores in the union'ed fuzzy set. Our preliminary results show that Bugzie achieves higher accuracy and efficiency than other state-of-the-art approaches.

Why so complicated? Simple term filtering and weighting for location-based bug report assignment recommendation

“…As a developer improves a software product he leaves evidence of his expertise in various software artifacts. In spite of the diversity of data that is available from a software project, bug report assignment recommendation approaches tend to focus on only one source of expertise information, commonly either bug reports [12], [17] or source revision commits [11]. If the data source is limited or not available, there may not be sufficient information for the use of a particular approach or the approach could have a low accuracy.…”

A Survey on Automatic Defect Report Triage

“…Tamrawi et al [18] used a Fuzzy Set to represent the developers who have the defect-fixing expertise applicable to a specific technical term. The defect reports previously fixed by developers are used to find capable developers to fix the defects.…”

“…Cubranic and Murphy [4] used summary and description of a defect report. Baysal et al [17] used the summary, the description, and the comments; Bhattacharya et al [7] and Tamrawi et al [18] used the bug report ID, the fixing developer ID, and summary as well as the description; Park et al [14] extracted features from the description of defect reports and its metadata (i.e. version, platform, and target milestone).…”

DRONE: Predicting Priority of Reported Bugs by Multi-factor Analysis

“…There are other lines of work that also analyze bug reports; these include the series of work on duplicate bug report detection [19], [23], [22], [28], bug localization [31], bug categorization [6], [8], [26], bug fix time prediction [12], [30], and bug fixer recommendation [10], [25]. Our work is also orthogonal to these studies.…”