scholarly journals Automatic dependent bug reports assembly for bug tracking systems by threshold-based similarity

2021 ◽  
Vol 23 (3) ◽  
pp. 1620
Author(s):  
B. Luaphol ◽  
J. Polpinij ◽  
M. Kaneampornpan
Keyword(s):  
State Of The Art ◽  
Cosine Similarity ◽  
Similarity Analysis ◽  
Tracking Systems ◽  
Problem Domain ◽  
Essential Information ◽  
Bug Reports ◽  
Bug Fixing ◽  
Bug Tracking ◽  
Software Bug

<p>Bug reports contain essential information for fixing problems that occur in software. Many studies have proposed methods for automatic analysis of bug reports. One such task could affect the completion of software bug fixing, known as “bug dependency”. Although this problem was mentioned by many researches, most of them discussed about the related bugs but not really dealt with dependency issue in bug reports. One possible solution used for addressing this issue is to assemble all relevant/dependent bug reports together before analysis of the next processing stages. This study presents a method of assembling dependent bug reports. The main mechanism is called “threshold-based similarity analysis”, and the three similarity techniques of cosine similarity (CS) multi aspect TF (MATF), and BM25 are compared with feedback, precision and likelihood value. As the BM25 with the threshold as 0.5 gives the best results, it was used to compare with the state of the art method. The results show that our method increases precision and likelihood values by 12% and 12.4% respectively. Therefore, our results can be used to encourage developers to recognize all dependent bugs in the same problem domain.</p>

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

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.

scholarly journals UNDERSTANDING THE DEVELOPER PARTICIPATION IN BUG FIX PROCESS

2014 ◽  
Vol 12 (8) ◽  
pp. 3823-3828
Author(s):  
Madhu Kumari ◽  
Meera Sharma ◽  
Nikita Yadav
Keyword(s):  
Linear Relationship ◽  
Open Source ◽  
Significant Variation ◽  
Open Source Project ◽  
Bug Reports ◽  
Bug Fixing ◽  
Bug Report ◽  
Severity Class ◽  
Software Bug

Prediction of the bug fix time in open source softwares is a challenging job. A software bug consists of many attributes that define the characteristics of the bug. Some of the attributes get filled at the time of reporting and some are  at the time of bug fixing. In this paper, 836 bug reports of two products namely Thunderbird and Webtools of Mozilla open source project have been considered. In  bug report, we see that there is no linear relationship among the bug attributes namely bug fix time, developers, cc count and severity. This paper has analyzed the interdependence among these attributes through graphical representation.The results conclude that :Case 1. 73% of bugs reported for Webtools are fixed by 17% developers and 61% of bugs are fixed by 14% developers for Thundebird.Case 2. We tried to find a relationship between the time taken by a developer in fixing a bug and the corresponding developer. We also observed that there is a significant variation in bug fixing process, bugs may take 1 day to 4 years in fixing.Case 3. There is no linear relationship between cc count i.e. manpower involved in bug fixing process and bug fix time.Case 4. Maximum number of developers are involved in fixing bugs for major severity class.

scholarly journals Severity Prediction for Bug Reports Using Multi-Aspect Features: A Deep Learning Approach

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1644
Author(s):  
Anh-Hien Dao ◽  
Cheng-Zen Yang
Keyword(s):  
Deep Learning ◽  
Matthews Correlation Coefficient ◽  
State Of The Art ◽  
Textual Information ◽  
Learning Framework ◽  
Bug Reports ◽  
Average Accuracy ◽  
Severity Prediction ◽  
Quality Aspect ◽  
Software Bug

The severity of software bug reports plays an important role in maintaining software quality. Many approaches have been proposed to predict the severity of bug reports using textual information. In this research, we propose a deep learning framework called MASP that uses convolutional neural networks (CNN) and the content-aspect, sentiment-aspect, quality-aspect, and reporter-aspect features of bug reports to improve prediction performance. We have performed experiments on datasets collected from Eclipse and Mozilla. The results show that the MASP model outperforms the state-of-the-art CNN model in terms of average Accuracy, Precision, Recall, F1-measure, and the Matthews Correlation Coefficient (MCC) by 1.83%, 0.46%, 3.23%, 1.72%, and 6.61%, respectively.

Severity Assessment of a Reported Bug by Considering its Uncertainty and Irregular State

2018 ◽  
Vol 9 (4) ◽  
pp. 20-46 ◽  
Author(s):  
Madhu Kumari ◽  
Meera Sharma ◽  
V. B. Singh
Keyword(s):  
Learning Strategies ◽  
State Of The Art ◽  
Tracking System ◽  
Machine Learning Techniques ◽  
Severity Assessment ◽  
Fast Speed ◽  
Learning Techniques ◽  
Bug Report ◽  
Bug Tracking ◽  
Software Bug

An accurate bug severity assessment is an important factor in bug fixing. Bugs are reported on the bug tracking system by different users with a fast speed. The size of software repositories is also increasing at an enormous rate. This increased size often has much uncertainty and irregularities. The factors that cause uncertainty are biases, noise and abnormality in data. The authors consider that software bug report phenomena on the bug tracking system keeps an irregular state. Without proper handling of these uncertainties and irregularities, the performance of learning strategies can be significantly reduced. To incorporate and consider these two phenomena, they have used entropy as an attribute to assess bug severity. The authors have predicted the bug severity by using machine learning techniques, namely KNN, J48, RF, RNG, NB, CNN and MLR. They have validated the classifiers using PITS, Eclipse and Mozilla projects. The results show that the proposed entropy-based approaches improves the performance as compared to the state of the art approach considered in this article.

The Influence Ranking for Testers in Bug Tracking Systems

2019 ◽  
Vol 29 (01) ◽  
pp. 93-113 ◽  
Author(s):  
Hui Li ◽  
Guofeng Gao ◽  
Rong Chen ◽  
Xin Ge ◽  
Shikai Guo ◽  
...  
Keyword(s):  
Tracking System ◽  
Tracking Systems ◽  
Software Projects ◽  
Actual Performance ◽  
Bug Reports ◽  
Existence Time ◽  
Bug Report ◽  
Bug Tracking ◽  
Node Removal ◽  
Bug Fixes

At present, bug tracking systems are used to collect and manage bug reports in many software projects. As participants, the testers not only submit bug reports to the system, but also comment on bug reports in the system. The tester’s behaviors of submitting and commenting reflect his/her influence in bug tracking systems. However, with the rapid increase of the bug reports in software projects, evaluating the testers’ influence in the projects accurately becomes more and more difficult. Aiming at solving this problem, the submission and comment on bug report can be regarded as social behaviors of the testers, and thus the method of Influence Ranking for Testers (IRfT) in bug tracking systems is presented and used for measuring the influence of the testers in this paper. The case study of the Eclipse project in Bugzilla shows that the result produced by IRfT is consistent with the actual performance of the testers in this project. The ranking results can keep stable in the cases of link adding or removing and tester removing in tester networks, and the results are also proved to be valid in the future. The further investigation on the speed of network break-down by node removal demonstrates that the top-ranking testers are important in the organization of tester networks. Additionally, the results also show that the ranking of the testers is related to the existence time in bug tracking system. Therefore, IRfT is proved to be an effective measurement for evaluating the influence of the testers in bug tracking system, and it can further demonstrate the testers’ contributions in software testing, such as bug validations, bug fixes, etc.

scholarly journals Using Categorical Features in Mining Bug Tracking Systems to Assign Bug Reports

2018 ◽  
Vol 9 (2) ◽  
pp. 29-39
Author(s):  
Mamdouh Alenezi ◽  
Shadi Banitaan ◽  
Mohammad Zarour
Keyword(s):  
Tracking Systems ◽  
Bug Reports ◽  
Bug Tracking

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

scholarly journals Assessment of Software Bug Complexity and Severity using Evolutionary SOM Scheme

2019 ◽  
Vol 8 (6) ◽  
pp. 3152-3158
Keyword(s):  
Software Quality ◽  
Software Maintenance ◽  
Negative Impact ◽  
Distance Functions ◽  
Tracking Systems ◽  
Software Defects ◽  
Bug Reports ◽  
Bug Report ◽  
Severity Prediction ◽  
Software Bug

The software defect prediction and assessment plays a significant role in the software development process. Predicting software defects in the earlier stages will increases the software quality, reliability and efficiency, the cost of detecting and eliminating software defects have been the most expensive task during both development and maintenance process, as software demands increase and delivery of the software span decreased, ensuring software quality becomes a challenge. However, due to inadequate testing, no software can pretend to be free from errors. Bug repositories are used for storing and managing bugs in software projects. A bug in the repositories is recorded as a bug report. When a bug is found by a tester its available information is entered in defect tracking systems. During its resolution process a bug enters into various bug states. These defect tracking systems enable user to give the information about the bugs while running the software. However, the severity prediction has recently gained a lot of attention in software maintenance. Bugs with greater severity should be resolved before bugs with lower severity. In this paper an evolutionary interactive scheme to evaluate bug reports and assess the severity is proposed. This paper presents a Software Bug Complexity Cluster (SBCC) using Self Organizing Maps. In this SBCC a feature matrix is built using bug durations and the complexities of software bugs are categorized into distinct clusters including Blocker, Critical, Major, Trivial and Minor by specifying negative impact of the defect using two different techniques, namely k-means and SOM. Bug duration, proximity error and pre-defined distance functions are used to estimate the accuracy of different bug complexities. Our systematic study found that SOM's proximity error and fitness have greater performance and efficiency than K-Means. The collected results showed better performance for the SBCC with respect to fitness and cluster proximity error.

Bug Tracking Systems as Public Spheres

2007 ◽  
Vol 11 (1) ◽  
pp. 32-41
Author(s):  
Joseph Reagle, ◽  
Keyword(s):  
Cultural Values ◽  
Classification Systems ◽  
Tracking Systems ◽  
Web Browser ◽  
Public Spheres ◽  
The Public ◽  
Bug Tracking ◽  
The Public Sphere ◽  
Normative Criteria ◽  
Software Bug

Based upon literature that argues technology, and even simple classification systems, embody cultural values, I ask if software bug tracking systems are similarly value laden. I make use of discourse within and around Web browser software development to identify specific discursive values, adopted from Ferree et al.'s "normative criteria for the public sphere," and conclude by arguing that such systems mediate community concerns and are subject to contested interpretations by their users.

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