An Analysis of Software Bug Reports Using Machine Learning Techniques

Tran, Ha Manh; Le, Son T.; Nguyen, Son C.; Ho, Phong Thanh

doi:10.1007/s42979-019-0004-1

Cited by 19 publications

(8 citation statements)

References 17 publications

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“…Therefore, the ratio between the occurrence frequency of keywords over the total number of keywords of a document (TF) and the inverse of the occurrence frequency of keywords over the total number of documents (IDF) is calculated. The product of both statistics results in TF-IDF values, representing a set of keywords which best describe the document [16].…”

Section: Natural Language Processingmentioning

confidence: 99%

What to prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development

Hang¹,

Dobler²,

Kühl³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Managing large numbers of incoming bug reports and finding the most critical issues in hardware development is time consuming, but crucial in order to reduce development costs. In this paper, we present an approach to predict the time to fix, the risk and the complexity of debugging and resolution of a bug report using different supervised machine learning algorithms, namely Random Forest, Naive Bayes, SVM, MLP and XGBoost. Further, we investigate the effect of the application of active learning and we evaluate the impact of different text representation techniques, namely TF-IDF, Word2Vec, Universal Sentence Encoder and XLNet on the model's performance. The evaluation shows that a combination of text embeddings generated through the Universal Sentence Encoder and MLP as classifier outperforms all other methods, and is well suited to predict the risk and complexity of bug tickets.

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Section: Natural Language Processingmentioning

confidence: 99%

What to prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development

Hang¹,

Dobler²,

Kühl³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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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%

“…Although the proposed approach worked better than the other, it did not yield good results. H. Manh et al [41]  …”

Section: Bug Repository Of Eclipse From 2001 To 2007mentioning

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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“…The bug reports contain several nominal and textual attributes [5]: platform, bugid, assignee, component, severity, status, priority, reporter, date of submission, product, resolution, operating system, cc-list, summary, and long description [3]. Among these, severity is considered the most important attribute for assigning priority to the bug report [6]. Severity has seven types: major, minor, blocker, normal, critical, trivial, and enhancement.…”

Section: Introductionmentioning

confidence: 99%

Bug Prioritization Using Average One Dependence Estimator

Saleem¹,

Naseem²,

Khan³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Automation software need to be continuously updated by addressing software bugs contained in their repositories. However, bugs have different levels of importance; hence, it is essential to prioritize bug reports based on their severity and importance. Manually managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolution of critical bugs. Therefore, bug report prioritization is vital. This study proposes a new model for bug prioritization based on average one dependence estimator; it prioritizes bug reports based on severity, which is determined by the number of attributes. The more the number of attributes, the more the severity. The proposed model is evaluated using precision, recall, F1-Score, accuracy, G-Measure, and Matthew's correlation coefficient. Results of the proposed model are compared with those of the support vector machine (SVM) and Naive Bayes (NB) models. Eclipse and Mozilla datasetswere used as the sources of bug reports. The proposed model improved the bug repository management and outperformed the SVM and NB models. Additionally, the proposed model used a weaker attribute independence supposition than the former models, thereby improving prediction accuracy with minimal computational cost.

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An Analysis of Software Bug Reports Using Machine Learning Techniques

Cited by 19 publications

References 17 publications

What to prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development

What to prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development

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

Bug Prioritization Using Average One Dependence Estimator

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