Towards Effective Bug Triage with Software Data Reduction Techniques

Xuan, Jifeng; Jiang, He; Hu, Yan; Zhang, Ren; Zou, Weiqin; Luo, Zhongxuan; Wu, Xindong

doi:10.1109/tkde.2014.2324590

Cited by 132 publications

(61 citation statements)

References 59 publications

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“…The four well-performed algorithms are chosen in text data [13,19] and software data, namely Information Gain (IG), χ2 statistic (CH) [14], Symmetrical Uncertainty attribute evaluation (SU), and Relief-F Attribute selection (RF). The chi-squared distribution also known as chi-square or χ² distribution with k degrees of freedom is the distribution of a sum of the squares of k independent criterion normal random variables.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Due to large number of daily bugs and lack of expertise of all the bugs, manual triage is an expensive in time cost and labor cost, low in accuracy. To overcome the limitations of existing work, an automatic bug triage approaches proposed [19]. This approach applies the text classification techniques in order to predict the relevant developer for bug reports without tossing.…”

Section: Introductionmentioning

confidence: 99%

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An Approach for Predicting Bug Triage using Data Reduction Methods

Duraisamy¹,

Raja²,

Kandaswamy³

2017

IJCA

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Most of the software companies need to deal with large number of software bugs each and every day. Software bugs are inevitable and fixing software bugs is an expensive task. The proposed system employs the combination of data reduction techniques that is feature selection algorithm (FS) and instance selection algorithm (IS) in order to shrink the bug data set and also to upgrade the accuracy of bug triage. Predictive model is used to determine the order of reduction techniques for a new bug data set, i.e., to choose between FS to IS or IS to FS. The aim of effective bug triaging software is to assign potentially skilled developers to new coming bug reports. To decrease the manual and time cost, text classification techniques are applied to accomplish automatic bug triage approach aims to precisely predict the developer to solve or fix the new bug report. The proposed system performance is verified using Mozilla bug data set. To exhibit the effectiveness, scales of data set is reduced by using data reduction technique in order to decrease the time and labor cost, improve the accuracy of bug triage with high-quality bug data in software development and maintenance.

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Section: Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Approach for Predicting Bug Triage using Data Reduction Methods

Duraisamy¹,

Raja²,

Kandaswamy³

2017

IJCA

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“…Reported bugs should be analyzed to determine whether they are valid or not, correct or not, and unique or not. This is called a bug triage process [1].…”

Section: Introductionmentioning

confidence: 99%

“…In their study textual descriptions were transformed to feature vectors, which were the inputs of classification models. However, the study of [1] tells that not all the features are important for bug report prioritization. Their results motivated us to adopt feature selection methods on bug report prioritization.…”

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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“…For open source large-scale software projects, the number of daily bugs is so large which makes the triaging process very difficult and challenging [10]. Software companies spend over 45 percent of cost in fixing bugs .There are two challenges related to bug data that may affect the effective use of bug repositories in software development tasks, namely the large scale and the low quality.…”

mentioning

confidence: 99%

Bug Triage Using Dimensionality Reduction Technique And PSO Algorithm

Amritha¹,

Rani²

2016

IJECS

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Abstract-The process of fixing bug is bug triage that aims to properly assign a developer to a new bug. Software companies pay out most of their expenses in dealing with these bugs. To reduce time and cost of bug triaging, an automated approach is developed to predict a developer with relevant experience to solve the new coming report. In proposed approach data reduction is done on bug data set which will reduce the scale of the data as well as increase the quality of the data. Instance selection and feature selection is also used simultaneously with historical bug data. Previously, text classification techniques are applied to conduct bug triage. The problem here is to get quality bug data sets as they are of very huge in size. In the proposed system, the problems of reducing the size and to improve the quality of bug data are addressed.First, pre-processing is done to the remove unimportant attributes and to identify missing terms. Then instance selection is combined with feature selection by using Dimensionality reduction technique to simultaneously reduce data size on the bug dimension and the word dimension. By using PSO algorithm, the reduction order is determined using fitness value. It is used to produce quality bug data set. The results show that the proposed system can effectively reduce the data size and improve the accuracy of bug triage. The proposed system provides an approach to leveraging techniques on data processing to form reduced and high eminence bug data in software improvement and maintenance.

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Towards Effective Bug Triage with Software Data Reduction Techniques

Cited by 132 publications

References 59 publications

An Approach for Predicting Bug Triage using Data Reduction Methods

An Approach for Predicting Bug Triage using Data Reduction Methods

Exploring the Influence of Feature Selection Techniques on Bug Report Prioritization

Bug Triage Using Dimensionality Reduction Technique And PSO Algorithm

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