Supporting change request assignment in open source development

Canfora, Gerardo; Cerulo, Luigi

doi:10.1145/1141277.1141693

Cited by 97 publications

(76 citation statements)

References 17 publications

(13 reference statements)

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“…Canfora and Cerulo [1] proposed an approach to assign a developer to a new bug report, using historic information stored in the bug and source code repositories. To classify bugs according to developer, Aljarah et al [19] evaluated different term selection techniques to find discriminating terms from the summary field of a bug.…”

Section: Related Workmentioning

confidence: 99%

“…The effort (bug lifetime) to fix a new bug is predicted in [9,44]. Anvik [8] and Canfora et al [1] built a developer recommender which automatically assigns a developer to a new bug report. We combine these attributes with the attributes in BF to check whether this set of training features improves the classifier performance.…”

Section: Categorical Featuresmentioning

confidence: 99%

“…The text features of a bug report are formed by combining summary and long description of a bug, and have been used by different researchers [1,3,17,27] to perform their experiments. The reason for combining the two is that the summary attribute conveys important details about a bug which adds meaningful information to the long description.…”

Section: Text Features (Summary and Long Description)mentioning

confidence: 99%

“…In a bug repository, bugs are reported from different sources, e.g., from users, developers or any other member of the technical support team [1] . These bugs need to be analyzed carefully to determine, for example, whether the bugs are duplicate (reported earlier) or unique, valid or invalid (caused by the operating system, not by the product), important or unimportant, and who will resolve them.…”

Section: Introductionmentioning

confidence: 99%

“…Mining techniques find hidden patterns from the data stored in these repositories, thus revealing useful information. Different recommenders have been built by researchers to assist in bug triaging, e.g., for assigning a bug report to an appropriate developer for resolution [1,8] , for predicting the component for which a bug is reported [2] , for predicting the time that a bug will take to resolve [9][10] . However, there has been little work on recommending priority and severity for a new bug report [11][12][13] .…”

Section: Introductionmentioning

confidence: 99%

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Bug Prioritization to Facilitate Bug Report Triage

Kanwal

Maqbool

2012

J. Comput. Sci. Technol.

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The large number of new bug reports received in bug repositories of software systems makes their management a challenging task. Handling these reports manually is time consuming, and often results in delaying the resolution of important bugs. To address this issue, a recommender may be developed which automatically prioritizes the new bug reports. In this paper, we propose and evaluate a classification based approach to build such a recommender. We use the Naïve Bayes and Support Vector Machine (SVM) classifiers, and present a comparison to evaluate which classifier performs better in terms of accuracy. Since a bug report contains both categorical and text features, another evaluation we perform is to determine the combination of features that better determines the priority of a bug. To evaluate the bug priority recommender, we use precision and recall measures and also propose two new measures, Nearest False Negatives (NFN) and Nearest False Positives (NFP), which provide insight into the results produced by precision and recall. Our findings are that the results of SVM are better than the Naïve Bayes algorithm for text features, whereas for categorical features, Naïve Bayes performance is better than SVM. The highest accuracy is achieved with SVM when categorical and text features are combined for training.

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Section: Related Workmentioning

confidence: 99%

Section: Categorical Featuresmentioning

confidence: 99%

Section: Text Features (Summary and Long Description)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bug Prioritization to Facilitate Bug Report Triage

Kanwal

Maqbool

2012

J. Comput. Sci. Technol.

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Challenges and opportunities for software change request repositories: a systematic mapping study

Cavalcanti

Neto

Machado

et al. 2013

J Software Evolu Process

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Software maintenance starts as soon as the first artifacts are delivered and is essential for the success of the software. However, keeping maintenance activities and their related artifacts on track comes at a high cost. In this respect, change request (CR) repositories are fundamental in software maintenance. They facilitate the management of CRs and are also the central point to coordinate activities and communication among stakeholders. However, the benefits of CR repositories do not come without issues, and commonly occurring ones should be dealt with, such as the following: duplicate CRs, the large number of CRs to assign, or poorly described CRs. Such issues have led researchers to an increased interest in investigating CR repositories, by considering different aspects of software development and CR management. In this paper, we performed a systematic mapping study to characterize this research field. We analyzed 142 studies, which we classified in two ways. First, we classified the studies into different topics and grouped them into two dimensions: challenges and opportunities. Second, the challenge topics were classified in accordance with an existing taxonomy for information retrieval models. In addition, we investigated tools and services for CR management, to understand whether and how they addressed the topics identified.

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An empirical study on how expert knowledge affects bug reports

Rodeghero

Huo

Ding

et al. 2016

J Software Evolu Process

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Bug reports are crucial software artifacts for both software maintenance researchers and practitioners. A typical use of bug reports by researchers is to evaluate automated software maintenance tools: a large repository of reports is used as input for a tool, and metrics are calculated from the tool's output. But this process is quite different from practitioners, who distinguish between reports written by experts, such as programmers, and reports written by non-experts, such as users. Practitioners recognize that the content of a bug report depends on its author's expert knowledge. In this paper, we present an empirical study of the textual difference between bug reports written by experts and non-experts. We find that a significant difference exists and that this difference has a significant impact on the results from a state-of-the-art feature location tool. Through an additional study, we also found no evidence that these encountered differences were caused by the increased usage of terms from the source code in the expert bug reports. Our recommendation is that researchers evaluate maintenance tools using different sets of bug reports for experts and nonexperts.

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Supporting change request assignment in open source development

Cited by 97 publications

References 17 publications

Bug Prioritization to Facilitate Bug Report Triage

Bug Prioritization to Facilitate Bug Report Triage

Challenges and opportunities for software change request repositories: a systematic mapping study

An empirical study on how expert knowledge affects bug reports

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