EXPSOL: Recommending Online Threads for Exception-Related Bug Reports

Bug localization represents one of the most expensive, as well as time-consuming, activities during software maintenance and evolution. To alleviate the workload of developers, numerous methods have been proposed to automate this process and narrow down the scope of reviewing buggy files. In this paper, we present a novel buggy source-file localization approach, using the information from both the bug reports and the source files. We leverage the part-of-speech features of bug reports and the invocation relationship among source files. We also integrate an adaptive technique to further optimize the performance of the approach. The adaptive technique discriminates Top 1 and Top N recommendations for a given bug report and consists of two modules. One module is to maximize the accuracy of the first recommended file, and the other one aims at improving the accuracy of the fixed defect file list. We evaluate our approach on six large-scale open source projects, i.e. ASpectJ, Eclipse, SWT, Zxing, Birt and Tomcat. Compared to the previous work, empirical results show that our approach can improve the overall prediction performance in all of these cases. Particularly, in terms of the Top 1 recommendation accuracy, our approach achieves an enhancement from 22.73% to 39.86% for ASpectJ, from 24.36% to 30.76% for Eclipse, from 31.63% to 46.94% for SWT, from 40% to 55% for ZXing, from 7.97% to 21.99% for Birt, and from 33.37% to 38.90% for Tomcat.

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Mining Bug Report Repositories to Identify Significant Information for Software Bug Fixing

Applied Science and Engineering Progress ◽

10.14416/j.asep.2021.03.005 ◽

2021 ◽

Author(s):

Bancha Luaphol ◽

Jantima Polpinij ◽

Manasawee Kaenampornpan

Keyword(s):

The Other ◽

Problem Domain ◽

Significant Information ◽

Bug Reports ◽

Bug Fixing ◽

Classification Technique ◽

Bug Report ◽

Multiple Issues ◽

Improved Accuracy ◽

Software Bug

Most studies relating to bug reports aims to automatically identify necessary information from bug reports for software bug fixing. Unfortunately, the study of bug reports focuses only on one issue, but more complete and comprehensive software bug fixing would be facilitated by assessing multiple issues concurrently. This becomes a challenge in this study, where it aims to present a method of identifying bug reports at severe level from a bug report repository, together with assembling their related bug reports to visualize the overall picture of a software problem domain. The proposed method is called “mining bug report repositories”. Two techniques of text mining are applied as the main mechanisms in this method. First, classification is applied for identifying severe bug reports, called “bug severity classification”, while “threshold-based similarity analysis” is then applied to assemble bug reports that are related to a bug report at severe level. Our datasets are from three opensource namely SeaMonkey, Firefox, and Core:Layout downloaded from the Bugzilla. Finally, the best models from the proposed method are selected and compared with two baseline methods. For identifying severe bug reports using classification technique, the results show that our method improved accuracy, F1, and AUC scores over the baseline by 11.39, 11.63, and 19% respectively. Meanwhile, for assembling related bug reports using threshold-based similarity technique, the results show that our method improved precision, and likelihood scores over the other baseline by 15.76, and 9.14% respectively. This demonstrate that our proposed method may help increasing chance to fix bugs completely.

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Improving Priority Prediction for Bug Reports with Comment Features

2021 IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI) ◽

10.1109/seai52285.2021.9477547 ◽

2021 ◽

Author(s):

Anh-Hien Dao ◽

Cheng-Zen Yang

Keyword(s):

Bug Reports

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Predicting the Severity of Bug Reports Based on Feature Selection

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194018500158 ◽

2018 ◽

Vol 28 (04) ◽

pp. 537-558 ◽

Cited By ~ 4

Author(s):

Wenjie Liu ◽

Shanshan Wang ◽

Xin Chen ◽

He Jiang

Keyword(s):

Feature Selection ◽

Software Maintenance ◽

Feature Selection Method ◽

Selection Methods ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Bug Reports ◽

Single Feature ◽

Bug Report ◽

Severity Prediction

In software maintenance process, it is a fairly important activity to predict the severity of bug reports. However, manually identifying the severity of bug reports is a tedious and time-consuming task. So developing automatic judgment methods for predicting the severity of bug reports has become an urgent demand. In general, a bug report contains a lot of descriptive natural language texts, thus resulting in a high-dimensional feature set which poses serious challenges to traditionally automatic methods. Therefore, we attempt to use automatic feature selection methods to improve the performance of the severity prediction of bug reports. In this paper, we introduce a ranking-based strategy to improve existing feature selection algorithms and propose an ensemble feature selection algorithm by combining existing ones. In order to verify the performance of our method, we run experiments over the bug reports of Eclipse and Mozilla and conduct comparisons with eight commonly used feature selection methods. The experiment results show that the ranking-based strategy can effectively improve the performance of the severity prediction of bug reports by up to 54.76% on average in terms of [Formula: see text]-measure, and it also can significantly reduce the dimension of the feature set. Meanwhile, the ensemble feature selection method can get better results than a single feature selection algorithm.

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