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

Tian, Yuan; Lo, David; Sun, Chengnian

doi:10.1109/icsm.2013.31

Cited by 118 publications

(69 citation statements)

References 25 publications

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“…Lamkanfi et al propose a prediction model to classify severity of bug reports in open source systems, but consider only two severity labels: severe or not [19]. Tian et al propose DRONE, a multi factor analysis technique to classify the priority of bug reports [34]. Their approach outperforms Menzies and Marcus's approach substantially in terms of Fmeasure.…”

Section: A Classification Of Issue Reportsmentioning

confidence: 99%

Active Semi-supervised Defect Categorization

Thung

2015

2015 IEEE 23rd International Conference on Program Comprehension

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Abstract-Defects are inseparable part of software development and evolution. To better comprehend problems affecting a software system, developers often store historical defects and these defects can be categorized into families. IBM proposes Orthogonal Defect Categorization (ODC) which include various classifications of defects based on a number of orthogonal dimensions (e.g., symptoms and semantics of defects, root causes of defects, etc.). To help developers categorize defects, several approaches that employ machine learning have been proposed in the literature. Unfortunately, these approaches often require developers to manually label a large number of defect examples. In practice, manually labelling a large number of examples is both time-consuming and labor-intensive. Thus, reducing the onerous burden of manual labelling while still being able to achieve good performance is crucial towards the adoption of such approaches. To deal with this challenge, in this work, we propose an active semi-supervised defect prediction approach. It is performed by actively selecting a small subset of diverse and informative defect examples to label (i.e., active learning), and by making use of both labeled and unlabeled defect examples in the prediction model learning process (i.e., semi-supervised learning). Using this principle, our approach is able to learn a good model while minimizing the manual labeling effort.To evaluate the effectiveness of our approach, we make use of a benchmark dataset that contains 500 defects from three software systems that have been manually labelled into several families based on ODC. We investigate our approach's ability in achieving good classification performance, measured in terms of weighted precision, recall, F-measure, and AUC, when only a small number of manually labelled defect examples are available. Our experiment results show that our active semi-supervised defect categorization approach is able to achieve a weighted precision, recall, F-measure, and AUC of 0.651, 0.669, 0.623, and 0.710, respectively, when only 50 defects are manually labelled. Furthermore, it outperforms an existing active multiclass classification algorithm, proposed in the machine learning community, by a substantial margin.

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Section: A Classification Of Issue Reportsmentioning

confidence: 99%

Active Semi-supervised Defect Categorization

Thung

2015

2015 IEEE 23rd International Conference on Program Comprehension

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“…In this way, a keyword dictionary is built. (5). Keyword vector modeling: This step involves the construction of a keyword vector model (KV).…”

Section: Examplementioning

confidence: 99%

“…Several previous studies have been conducted to investigate the classification of issue reports for open-source projects using supervised machine learning algorithms [4][5][6][7]. Feng et al [8,9] proposed test report prioritization methods for use in crowdsourced testing.…”

Section: Introductionmentioning

confidence: 99%

Using Knowledge Transfer and Rough Set to Predict the Severity of Android Test Reports via Text Mining

Guo

Chen

2017

Symmetry

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Abstract:Crowdsourcing is an appealing and economic solution to software application testing because of its ability to reach a large international audience. Meanwhile, crowdsourced testing could have brought a lot of bug reports. Thus, in crowdsourced software testing, the inspection of a large number of test reports is an enormous but essential software maintenance task. Therefore, automatic prediction of the severity of crowdsourced test reports is important because of their high numbers and large proportion of noise. Most existing approaches to this problem utilize supervised machine learning techniques, which often require users to manually label a large number of training data. However, Android test reports are not labeled with their severity level, and manual labeling is time-consuming and labor-intensive. To address the above problems, we propose a Knowledge Transfer Classification (KTC) approach based on text mining and machine learning methods to predict the severity of test reports. Our approach obtains training data from bug repositories and uses knowledge transfer to predict the severity of Android test reports. In addition, our approach uses an Importance Degree Reduction (IDR) strategy based on rough set to extract characteristic keywords to obtain more accurate reduction results. The results of several experiments indicate that our approach is beneficial for predicting the severity of android test reports.

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“…For example, DRONE [2] analyzed the textual description, author and product of bug reports to assign priorities. Kremenet et al [3] checked the success and failure of a bug report to prioritize reports.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Influence of Feature Selection Techniques on Bug Report Prioritization

Wang¹,

Zhang

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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To improve software quality, developers often open a bug repository and allow users to find bugs, describe bugs in the form of bug reports and submit bug reports to the repository. Based on the description, testers assign a priority to each bug report. In the beginning the process of priority assignment is performed manually. With the increasing amount of bug reports, researchers introduced classification methods to assign priorities automatically with all the features considered. In this paper feature selection methods are introduced to improve the effect of bug report prioritization using classification models. The experimental results show that feature selection based on Information Gain and Pearson Correlation can improve the precision and recall for bug report prioritization on two models, i.e., SVM and Naive Bayes.

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

Cited by 118 publications

References 25 publications

Active Semi-supervised Defect Categorization

Active Semi-supervised Defect Categorization

Using Knowledge Transfer and Rough Set to Predict the Severity of Android Test Reports via Text Mining

Exploring the Influence of Feature Selection Techniques on Bug Report Prioritization

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