scholarly journals MULTI-FEATURE MODEL FOR DUPLICATE BUG REPORTS DETECTION SOLUTION

2019 ◽  
Vol 1 (1) ◽  
pp. 29-36
Author(s):  
Phuc Minh Nhan ◽  
Thien Hoang Duy Nguyen
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Maintenance ◽  
Feature Model ◽  
Software Bugs ◽  
Bug Reports ◽  
Duplicate Bug Reports ◽  
Error Reports ◽  
Bug Management

For open source software such as Firefox, Eclipse, Subversion,. . . they usually have a system for bug management that sent by users. These bug reports help the system identify various software bugs which makes software maintenance better. However, a situation occurs that there are many error reports sent to  the processing repository that these bug reports were previously reported by different users, this is called duplicate bug reports. In this paper, we introduce a multi-feature model combined with weighted improvements from CFC (ClassFeature-Centroid) to detect duplicate bug reportsautomatically. We have experimented on three projects of Mozilla, Eclipse and OpenOffice. The results show that our method can improve 8-12% better as compared to the compared methods.

scholarly journals Mining Severe Priority Bugs in Software Maintenance

2019 ◽  
Vol 8 (4S2) ◽  
pp. 729-732
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Maintenance ◽  
Maintenance Phase ◽  
Bug Reports ◽  
Bug Tracking ◽  
The Right ◽  
Software Bug ◽  
Positive Results

Maintenance of open source software is a hectic task as the number of bugs reported is huge. The number of projects, components and versions in an open source project also contribute to the number of bugs that are being reported. Classification of bugs based on priority and identification of the suitable engineers for assignment of bugs for such huge systems still remains a major challenge. Bugs that are misclassified or assigned to engineers who don’t have the component expertise, drastically affect the time taken towards bug resolution. In this paper we have explored the usage of data mining techniques on the classification of bugs and assignment of bugs to engineers.Our focus was on classifying bugs as either severe or non-severe and identification of engineers who have the right expertise to fix the bugs. The prediction of bug severity and identification of engineers were done by mining bug reports from JIRA, an open source software bug tracking tool. The mining process yielded positive results and will be a decision enhancer for severe bugs in the maintenance phase

PRST: A PageRank-Based Summarization Technique for Summarizing Bug Reports with Duplicates

2017 ◽  
Vol 27 (06) ◽  
pp. 869-896 ◽  
Author(s):  
He Jiang ◽  
Najam Nazar ◽  
Jingxuan Zhang ◽  
Tao Zhang ◽  
Zhilei Ren
Keyword(s):  
Regression Model ◽  
Software Maintenance ◽  
State Of The Art ◽  
Similarity Metrics ◽  
Automatic Summarization ◽  
Textual Information ◽  
Additional Information ◽  
Bug Reports ◽  
Reference Process ◽  
Duplicate Bug Reports

During software maintenance, bug reports are widely employed to improve the software project’s quality. A developer often refers to stowed bug reports in a repository for bug resolution. However, this reference process often requires a developer to pursue a substantial amount of textual information in bug reports which is lengthy and tedious. Automatic summarization of bug reports is one way to overcome this problem. Both supervised and unsupervised methods are effectively proposed for the automatic summary generation of bug reports. However, existing methods disregard the significance of duplicate bug reports in summarizing bug reports. In this study, we propose a PageRank-based Summarization Technique (PRST), which utilizes the textual information contained in bug reports and additional information in associated duplicate bug reports. PRST uses three variants of PageRank-based on Vector Space Model (VSM), Jaccard, and WordNet similarity metrics. These variants are utilized to calculate the textual similarity of the sentences between the master bug reports and their duplicates. PRST further trains a regression model and predicts the probability of sentences belonging to the summary. Finally, we combine the values of PageRank and regression model scores to rank the sentences and produce the summary for the master bug reports. In addition, we construct two corpora of bug reports and duplicates, i.e. MBRC and OSCAR. Empirical results suggest that PRST outperforms the state-of-the-art method BRC in terms of Precision, Recall, F-score, and Pyramid Precision. Meanwhile, PRST with WordNet achieves the best results against PRST with VSM and Jaccard.

A Commit Messages-Based Bug Localization for Android Applications

2019 ◽  
Vol 29 (04) ◽  
pp. 457-487 ◽  
Author(s):  
Tao Zhang ◽  
Wenjun Hu ◽  
Xiapu Luo ◽  
Xiaobo Ma
Keyword(s):  
Open Source ◽  
Software Maintenance ◽  
State Of The Art ◽  
Bug Localization ◽  
Two Phase ◽  
Android Apps ◽  
Bug Reports ◽  
Android Applications ◽  
The Given ◽  
General Method

Recently, there has been consistent growth in Android applications (apps). Under these circumstances, software maintenance for Android apps becomes an essential and important task. The core of software maintenance is to locate bugs in source files. Previous bug localization approaches mainly focus on open-source desktop software (e.g. Eclipse, Mozilla, GCC). Even though a few studies locate the bugs in the Android apps, they are dedicated to a special app named ZXing, without developing a general method to locate the bugs in Android apps by taking into account the unique characteristics of Android apps’ bug reports. Such characteristics include fewer number of historical bug reports, insufficient detailed description, etc. These characteristics hinder existing localization approaches from being directly delivered to Android apps, because lack of enough information degrades the performance of those localization approaches relying on historical bug reports. Commit messages include more informative data which can provide the details of reported bugs. Therefore, in this paper, we propose a novel information retrieval-based approach which utilizes commit messages to locate new bugs in Android apps. This approach not only considers the structured textual similarity between the given bug and the candidate source files, but also computes the unstructured textual similarities between the new bug and the commit messages linked to the corresponding source files. According to the experimental results on 10 popular open-source Android apps managed by GitHub, our approach outperforms the state-of-the-art bug localization methods that include BugLocator, BLUiR, and two-phase model.

scholarly journals IMPROVING DETECTION PERFORMANCE OF DUPLICATE BUG REPORTS USING EXTENDED CLASS CENTROID INFORMATION

2019 ◽  
Vol 1 (26) ◽  
pp. 71-79
Author(s):  
Phuc Minh Nhan
Keyword(s):  
Software Maintenance ◽  
Software Projects ◽  
Centroid Method ◽  
Detection Scheme ◽  
Bug Reports ◽  
Software Packages ◽  
Extended Class ◽  
Detection Schemes ◽  
Class Centroid ◽  
Duplicate Bug Reports

In software maintenance, bug reports play an important role in the correctness of  software packages. Unfortunately, the duplicatebug report problem arises because there are too many duplicate bug reports in various software projects. Handling with duplicate bug reports is thus time-consuming and has high cost of software maintenance. Therefore, this research introduces a detection scheme based on the extended class centroid information (ECCI) to enhance thedetection performance. This method is extended from the previous one, which used only centroid method without considering the effects of both inner and inter class. Besides, this method also improved the previous use of normalized cosine in identifying the similarity between two bug reports by denormalized cosine.  The effectiveness of ECCI is proved through the empirical study with three open-source projects: SVN, Argo UML and Apache. The experimental results show thatECCI outperforms other detection schemes by about 10% in all cases.

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