Software Bug Categorization Technique Based on Fuzzy Similarity

Panda, Rama Ranjan; Nagwani, Naresh Kumar

doi:10.1109/iacc48062.2019.8971599

Cited by 8 publications

(9 citation statements)

References 13 publications

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“…Chawla and Singh (2018) proposed a semi-supervised fuzzy C-mean based approach for the classification of bug reports. Panda and Nagwani (2019) proposed a bug report defect classification method based on a fuzzy similarity metric, which calculates the fuzzy similarity of software defects and binary classifies bug reports according to user-defined thresholds. However, the classification performance of the above methods is relatively low compared with the supervised classification method.…”

Section: Related Workmentioning

confidence: 99%

Classification of open source software bug report based on transfer learning

et al. 2022

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Currently, the feature richness of text encoding vectors in the bug report classification model based on deep learning is limited by the size of the domain dataset and the quality of the text. However, it is difficult to further enrich the features of text encoding vectors. At the same time, most existing bug report classification methods ignore the submitter's personal information. To solve these problems, we construct nine personal information characteristics of bug report submitters in GitHub by survey. Then, we propose a GitHub bug report classification method named personal information fine‐tuning network (PIFTNet) based on transfer learning and the submitter's personal information. PIFTNet transfers the general text feature vectors in bidirectional encoder representation from transformers (BERT) to the domain of bug report classification by fine‐tuning the pre‐training parameters in BERT. It also combines the text characteristics and the characteristics of the submitter's personal information to construct the classification model. In addition, we propose a two‐stage training method to alleviate the catastrophic changes in the pre‐training parameters and loss of the initially learned knowledge caused by direct training of PIFTNet. We verify the proposed PIFTNet on the dataset extracted from GitHub and empirical results prove the effectiveness of PIFTNet.

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Section: Related Workmentioning

confidence: 99%

Classification of open source software bug report based on transfer learning

et al. 2022

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“…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

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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.

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“…Whereas, two of the surveyed papers have used unsupervised machine learning techniques: clustering [13], and association rule mining [18]. Only three papers [5,14,19] have used fuzzy logic-based approaches to support the current research problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, according to literature, it is unfair to hardly classifying a bug report into just two categories. Conversely, it is likely that bug report may have a certain degree of membership to both categories [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Classifying bug reports to bugs and other requests: an approach using topic modelling and fuzzy set theory

2021

IJACR

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Introduction 1.1OverviewBug Tracking Systems like Bugzilla, Gnats and Jira [1,2] are a type of systems that are used to support the maintenance activities during the lifecycle of software systems [3]. They are a communication mean between users and developers to report and fix issues [4]. In particular, these systems commonly manage the following two types of reported requests: corrective requests (bugs), and perfective requests (non-bugs) such as new feature request, improvement request, documentation, and so on [3][4][5][6][7]. Software bugs are unavoidable. Therefore, more and more bug reports are continually submitted to bug tracking systems. Specifically, statistical information in [8] states that more than 30 maintenance requests are reported daily.

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Software Bug Categorization Technique Based on Fuzzy Similarity

Cited by 8 publications

References 13 publications

Classification of open source software bug report based on transfer learning

Classification of open source software bug report based on transfer learning

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

Classifying bug reports to bugs and other requests: an approach using topic modelling and fuzzy set theory

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