CNN-Based Automatic Prioritization of Bug Reports

Umer, Qasim; Hui, Lam; Illahi, Inam

doi:10.1109/tr.2019.2959624

Cited by 64 publications

(41 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years the use of convolution models has been increased for solving different software engineering problems such as severity and priority prediction (Ramay, Umer, Yin, Zhu, & Illahi, 2019;Umer, Liu, & Illahi, 2019), duplicate bug retirval and bug report summarization (Deshmukh, Annervaz, Podder, Sengupta, & Dubash, 2017;Kalchbrenner, et al, 2014;X. Li, Jiang, Liu, Ren, & Li, 2018).…”

Section: Methodsmentioning

confidence: 99%

An Analysis of Deep Neural Network for Recommending Developers to Fix Reported Bugs

Abro¹,

Rehman²,

Das³

et al. 2021

European Journal of Science and Technology

View full text Add to dashboard Cite

Occurrence of bugs during the production cycle of software projects is a serious concern of the present time. According to an estimate, a very large number of bugs are recorded while dealing with complex and popular software releases. To locate these bugs and to solve them in efficient manner software industries incorporate the process of bug triage in software testing. Bug triage is intended to recommend the bug reports to an appropriate developer effectively to fix them successfully. However, it becomes labor-intensive and expensive to manually allocate these bug reports to the developer. Deep learning methods have been extensively used and experimented to various domains such as medical diagnosis, earthquake prediction and many more. To handle the above said bugs concerns, many studies have been carried out in order to automate the bug triaging process. Several researchers have directed their efforts by applying deep learning methods in different settings for autonomous recommendation for developers to remove or fix their bugs. In this paper we have proposed a Convolutional Neural Network model for recommending Top 10 developers to fix the reported bugs. For better performance of the model Word2Vec and Glove embeddings are combined with the neural network. The performance of CNN+Word2vec and CNN+Glove models is calculated by averaging the accuracy for 10 developers at five distinct learning rates. The reported results demonstrate that the combination of Convolution with word2vec embedding gives better average accuracy in the testing phase.

show abstract

Section: Methodsmentioning

confidence: 99%

An Analysis of Deep Neural Network for Recommending Developers to Fix Reported Bugs

Abro¹,

Rehman²,

Das³

et al. 2021

European Journal of Science and Technology

View full text Add to dashboard Cite

show abstract

“…An automated approach for predicting bug priority and severity using machine learning classification algorithms was investigated by H. Manh et al [41] Existing research is mostly focused on either automating bug categorization [11], [33] or bug prioritization [34], [35]. Limited work has been found in the area of categorization and prioritization of bug reports simultaneously [36] and therefore we present CaPBug framework that automates both bug categorization and bug prioritization.…”

Section: B Bug Prioritizationmentioning

confidence: 99%

“…In most of the available datasets, category and priority information is missing. Furthermore, most of the available research is focused on one problem independently, i.e., either automating bug categorization [11], [33] or bug prioritization [34], [35]. Consequently, very limited work has been done in the area of categorization and prioritization of bug reports simultaneously [36].…”

Section: Introductionmentioning

confidence: 99%

CaPBug-A Framework for Automatic Bug Categorization and Prioritization Using NLP and Machine Learning Algorithms

2021

View full text Add to dashboard Cite

Bug reports facilitate software development teams in improving the quality of software. These reports include significant information related to problems encountered within a software, possible enhancement suggestions, and other potential issues. Bug reports are typically complex and are too detailed; hence a lot of resources are required to analyze and process them manually. Moreover, it leads to delays in the resolution of high priority bugs. Accurate and timely processing of bug reports based on their category and priority plays a significant role in improving the quality of software maintenance. Therefore, an automated process of categorization and prioritization of bug reports is needed to address the aforementioned issues. Automated categorization and prioritization of bug reports have been explored recently by many researchers; however, limited progress has been made in this regard. In this research, we present a novel framework, titled CaPBug, for automated categorization and prioritization of bug reports. The framework is implemented using Natural Language Processing (NLP) and supervised machine learning algorithms. A baseline corpus is built with six categories and five prioritization levels by analyzing more than 2000 bug reports of Mozilla and Eclipse repository. Four classification algorithms i.e., Naive Bayes, Random Forest, Decision Tree, and Logistic Regression have been used to categorize and prioritize bug reports. We demonstrate that the CaPBug framework achieved an accuracy of 88.78% by using a Random Forest classifier with a textual feature for predicting the category. Similarly, using the CaPBug framework, an accuracy of 90.43% was achieved in predicting the priority of bug reports. Synthetic Minority Over-Sampling Technique (SMOTE) has been applied to address the class imbalance issue in priority classes.

show abstract

“…After that, the trained models are evaluated separately with the same sT est. Note that, we evaluate the performance of DCAR and Maalej's using the wellknown and most adopted metrics for machine learning classification [7], [14], [15], [18], i.e., precision, recall, and f-measure.…”

Section: ) Rq1:comparison Against the State Of The Artmentioning

confidence: 99%

“…In this regard, we exploit the Naive Bayes (NB), Multi-nomial Naive Bayes (MNB), Decision Tree (DT), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short Term Memory (L-STM) and repeat the evaluation as mentioned in Section III-C1. Note that we select these classifiers due to their significant performance for the textual classification [7], [14], [15], [18], [19].…”

Section: ) Rq4: Comparison Among Different Classifiersmentioning

confidence: 99%

Convolutional Neural Network Based Classification of App Reviews

et al. 2020

View full text Add to dashboard Cite

An app store (i.e., Google Play) is a platform for mobile apps for almost every software and service. App stores allow users to browse and download apps and facilitate developers to keep an eye on their apps by providing ratings and reviews of the apps. App reviews may include the user's experience, information about bugs, request for new features, or rating of the app in word. The manual categorization of app reviews is critical and time-consuming for developers. Automatic classification of app reviews may help developers especially for fixing bugs on time. In this perspective, several approaches have been proposed for the automatic classification of reviews. However, none of them exploits the non-textual information of app reviews. In this paper, we propose a deep learning based approach for the classification of app reviews. It does not only leverage non-textual information of app reviews but also exploits a deep learning technique that has proved more accurate for the text classification in various domains. The approach first extracts textual and nontextual information of each app review, preprocesses the textual information, computes the sentiment of app reviews using Senti4SD, and determines the history of the reviewer includes the total number of reviews posted by the reviewer, and his submission rate (i.e., what percentages of his review have been submitted for the associated app). Second, we create a digital vector against each app review. Finally, we train a deep learning based multi-class classifier to classify app reviews. The proposed approach is evaluated on a public dataset, and the results suggest that it significantly improves the state of the art. It improves average precision from 75.72% to 95.49%, average recall from 69.40% to 93.94%, and f-measure from 72.41% to 94.71%, respectively.

show abstract

CNN-Based Automatic Prioritization of Bug Reports

Cited by 64 publications

References 38 publications

An Analysis of Deep Neural Network for Recommending Developers to Fix Reported Bugs

An Analysis of Deep Neural Network for Recommending Developers to Fix Reported Bugs

CaPBug-A Framework for Automatic Bug Categorization and Prioritization Using NLP and Machine Learning Algorithms

Convolutional Neural Network Based Classification of App Reviews

Contact Info

Product

Resources

About