57 research outputs found

    Bug Fix Time Optimization Using Matrix Factorization and Iterative Gale-Shaply Algorithms

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    Bug triage is an essential task in software maintenance phase. It assigns developers (fixers) to bug reports to fix them. This process is performed manually by a triager, who analyzes developers profiles and submitted bug reports to make suitable assignments. Bug triaging process is time consuming thus automating this process is essential to improve the quality of software. Previous work addressed triaging problem either as an information retrieval or classification problem. This paper tackles this problem as a resource allocation problem, that aims at the best assignments of developers to bug reports, that reduces the total fixing time of the newly submitted bug reports, in addition to the even distribution of bug reports over developers. In this paper, a combination of matrix factorization and Gale Shapely algorithm, supported by the differential evolution is firstly introduced to optimize the total fix time and normalize developers work load. Matrix factorization is used to establish a recommendation system for Gale-Shapley to make assignment decisions. Differential evolution provides the best set of weights to build developers score profiles. The proposed approach is assessed over three repositories, Linux, Apache and Eclipse. Experimental results show that the proposed approach reduces the bug fixing time, in comparison to the manual triage, by 80.67%, 23.61% and 60.22% over Linux, Eclipse and Apache respectively. Moreover, the workload for the developers is uniform.Comment: 14 page, 7 figures, 8 tables, 10 equation

    Fuzzy set and cache-based approach for bug triaging

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    Software bugs are inevitable and bug fixing is an essential and costly phase during software development. Such defects are often reported in bug reports which are stored in an issue tracking system, or bug repository. Such reports need to be assigned to the most appropriate developers who will eventually fix the issue/bug reported. This process is often called Bug Triaging. Manual bug triaging is a difficult, expensive, and lengthy process, since it needs the bug triager to manually read, analyze, and assign bug fixers for each newly reported bug. Triagers can become overwhelmed by the number of reports added to the repository. Time and efforts spent into triaging typically diverts valuable resources away from the improvement of the product to the managing of the development process. To assist triagers and improve the bug triaging efficiency and reduce its cost, this thesis proposes Bugzie, a novel approach for automatic bug triaging based on fuzzy set and cachebased modeling of the bug-fixing capability of developers. Our evaluation results on seven large-scale subject systems show that Bugzie achieves significantly higher levels of efficiency and correctness than existing state-of-the-art approaches. In these subject projects, Bugzie\u27s accuracy for top-1 and top-5 recommendations is higher than those of the second best approach from 4-15% and 6-31%, respectively as Bugzie\u27s top-1 and top-5 recommendation accuracy is generally in the range of 31-51% and 70-83%, respectively. Importantly, existing approaches take from hours to days (even almost a month) to finish training as well as predicting, while in Bugzie, training time is from tens of minutes to an hour

    Towards Effective Bug Triage with Software Data Reduction Techniques

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    International audienceSoftware companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the problem of data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data. We combine instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance

    Effective Bug Triage based on Historical Bug-Fix Information

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    International audienceFor complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this pa-per, we describe our study on applying conventional bug triage techniques to projects of different sizes. We find that the effectiveness of a bug triage technique largely depends on the size of a project team (measured in terms of the number of developers). The conventional bug triage methods become less effective when the number of developers increases. To further improve the effectiveness of bug triage for large projects, we propose a novel recommendation method called BugFixer, which recommends developers for a new bug report based on historical bug-fix in-formation. BugFixer constructs a Developer-Component-Bug (DCB) network, which models the relationship between developers and source code components, as well as the relationship be-tween the components and their associated bugs. A DCB network captures the knowledge of "who fixed what, where". For a new bug report, BugFixer uses a DCB network to recommend to triager a list of suitable developers who could fix this bug. We evaluate BugFixer on three large-scale open source projects and two smaller industrial projects. The experimental results show that the proposed method outperforms the existing methods for large projects and achieves comparable performance for small projects

    Enhanced Approach for Bug Severity Prediction: Experimentation and Scope for Improvements

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    Software development is an iterative process, where developers create, test, and refine their code until it is ready for release. Along the way, bugs and issues are inevitable. A bug can be any error identified in requirement specification, design or implementation of any project. These bugs need to be categorized and assigned to developers to be resolved. the number of bugs generated in any large scale project are vast in number. These bugs can have significant or no impact on the project depending on the type of bug. The aim of this study is to develop a deep learning-based bug severity prediction model that can accurately predict the severity levels of software bugs. This study aims to address the limitations of the current manual bug severity assessment process and provide an automated solution using various classifiers e.g. NaĂŻve Bayes, Logistic regression, KNN and Support vector machine along with Mutual information as feature selection method, that can assist software development teams in giving severity code to bugs effectively. It seeks to improve the overall software development process by reducing the time and effort required for bug resolution and enhancing the quality and reliability of software

    Bug Triaging with High Confidence Predictions

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    Correctly assigning bugs to the right developer or team, i.e., bug triaging, is a costly activity. A concerted effort at Ericsson has been done to adopt automated bug triaging to reduce development costs. We also perform a case study on Eclipse bug reports. In this work, we replicate the research approaches that have been widely used in the literature including FixerCache. We apply them on over 10k bug reports for 9 large products at Ericsson and 2 large Eclipse products containing 21 components. We find that a logistic regression classifier including simple textual and categorical attributes of the bug reports has the highest accuracy of 79.00% and 46% on Ericsson and Eclipse bug reports respectively. Ericsson’s bug reports often contain logs that have crash dumps and alarms. We add this information to the bug triage models. We find that this information does not improve the accuracy of bug triaging in Ericsson’s context. Eclipse bug reports contain the stack traces that we add to the bug triaging model. Stack traces are only present in 8% of bug reports and do not improve the triage accuracy. Although our models perform as well as the best ones reported in the literature, a criticism of bug triaging at Ericsson is that accuracy is not sufficient for regular use. We develop a novel approach that only triages bugs when the model has high confidence in the triage prediction. We find that we improve the accuracy to 90% at Ericsson and 70% at Eclipse, but we can make predictions for 62% and 25% of the total Ericsson and Eclipse bug reports,respectively
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