Automated duplicate detection for bug tracking systems

Abstract

Bug tracking systems are important tools that guide the maintenance activities of software developers. The utility of these systems is hampered by an excessive number of duplicate bug reports-in some projects as many as a quarter of all reports are duplicates. Developers must manually identify duplicate bug reports, but this identification process is time-consuming and exacerbates the already high cost of software maintenance. We propose a system that automatically classifies duplicate bug reports as they arrive to save developer time. This system uses surface features, textual semantics, and graph clustering to predict duplicate status. Using a dataset of 29,000 bug reports from the Mozilla project, we perform experiments that include a simulation of a real-time bug reporting environment. Our system is able to reduce development cost by filtering out 8% of duplicate bug reports while allowing at least one report for each real defect to reach developers.

A Survey on Mining Software Repositories

“…There was a text-based analysis approach for detecting duplicate bugs [28], [29]. However, Wang et al increased the recall of duplicate bug detection by combining the methods from natural language and execution information [30].…”

Boosting a Rule-Based Chatbot Using Statistics and User Satisfaction Ratings

“…Identifying semantically similar questions entails at least two major difficulties: similarity measures targeted at longer documents are not suited to short texts such as regular questions; and word overlap measures (such as Dice's coefficient or the Jaccard similarity coefficient) cannot account for questions which mean the same but use different words. Notwithstanding, word overlap features have been shown to be efficient in certain settings [22,13]. CNN architectures, which, since their adoption from computer vision, have proved to be very successful feature extractors in text processing [9], have recently started to be applied to the task of DQD.…”

Categorizing and Analyzing the Impact of Bugs in Open Source Software

“…[Rank: Scary (8) [1] have conducted a survey on three major bug tacking systems namely APACHE, MOZILLA, AND ECLIPSE in order to find the information requirements and problem faced by developers in bug reporting system. N. Jalbert et al 2008 [2] have proposed a method that automatically categorizes redundant bug reports when they arrive to save developer time. Thomas Zimmermann et al 2009 [3] have addressed the problem of inadequately designed bug tracking systems in which information about bugs filtered out after numerous iterations of messages between user and developers.…”