Bug report severity level prediction in open source software: A survey and research opportunities

Gomes, Luiz Alberto Ferreira; Torres, Ricardo da Silva; Côrtes, Mário Lúcio

doi:10.1016/j.infsof.2019.07.009

Cited by 42 publications

(39 citation statements)

References 29 publications

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“…From the literature review perspective, Gomes et al [2] conducted a mapping study on automatic bug prioritization. The authors reported ten different aspects of the experiments from the selected studies.…”

Section: A Prioritization Of Bug Reportmentioning

confidence: 99%

“…In the bug repository, any developer, end-user, or tester can report the bug by filling a form of the specific repository. According to the published reports [2], [3], Eclipse [4] had 250,000 bug reports in 2009, while 84,245 bug reports have been submitted from 2013 to 2015 indication an average raise of 120 new bug reports on daily basis.…”

Section: Introductionmentioning

confidence: 99%

“…However, bug triaging is a manual task that makes the bug prioritization and assignment a laborious, inappropriate, and time-consuming process [3]. Moreover, manual bug triaging lacks in computing the correct priority, and assigning the bug to the most suitable developer [2]. Zhang et al [5] designed the lifecycle of the bug report as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

LCBPA: An Enhanced Deep Neural Network-Oriented Bug Prioritization and Assignment Technique Using Content-Based Filtering

2021

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Software maintenance is an important phase of a development life cycle that needs to be essentially performed in order to avoid the software failure. To systematically handle the bugs (defects), the software development organization develops a bug report that demonstrates the vulnerabilities from the software under test. However, manually handling the bug reports is a laborious, tedious, and time-consuming task. Moreover, the bug repository receives large numbers of bug reports on daily basis, which demands to timely fix the found and received bugs. Motivated by this, current work proposes an automated bug prioritization and assignment technique, called LCBPA (Long short-term memory, Content-based filtering for Bug Prioritization and Assignment). To perform the bug prioritization, we employed Long Short-Term Memory (LSTM) to predict the priority of the bug report. In contrast, for bug assignment, we used contentbased filtering, where the prioritized bug reports are automatically assigned to the developers based on their previous knowledge. The performance of the proposed bug prioritization model is determined by comparing with the state-of-the-art bug prioritization techniques using precision, recall and f1-score. Similarly, the effectiveness of the bug assignment model is evaluated by defining various case scenarios. The results show that the proposed LCBPA technique outperforms the current state-of-the-art bug prioritization techniques (with a 22% increase in F1-score), and also efficiently handles the bug assignment problem compared to the existing bug assignment techniques.

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Section: A Prioritization Of Bug Reportmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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LCBPA: An Enhanced Deep Neural Network-Oriented Bug Prioritization and Assignment Technique Using Content-Based Filtering

2021

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“…Staked denoising auto-encoder (SDA) is a method of feature learning that is used for DR.They have reported that BPDET has out-performed on most of the datasets and BPDET is tested using Wilcoxon rank-sum test and as a result, null hypothesis is rejected at α = 0.025. Gomes et al [43] performed a comprehensive mapping study in which he had reviewed recent research efforts on automatically bug report severity prediction and for this he had selected 54 papers. They reported that 27 studies are addressing bug report severity prediction on Free or Libre Open Source Software.…”

Section: Related Workmentioning

confidence: 99%

Hyper-Parameter Optimization of Classifiers, Using an Artificial Immune Network and Its Application to Software Bug Prediction

et al. 2020

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Software testing is an important task in software development activities, and it requires most of the resources, namely, time, cost and effort. To minimize this fatigue, software bug prediction (SBP) models are applied to improve the software quality assurance (SQA) processes by predicting buggy components. The bug prediction models use machine learning classifiers so that bugs can be predicted in software components in some software metrics. These classifiers are characterized by some configurable parameters, called hyperparameters that need to be optimized to ensure better performance. Many methods have been proposed by researchers to predict the defective components but these classifiers sometimes not perform well when default settings are used for machine learning classifiers. In this paper, software bug prediction model is proposed which uses machine learning classifiers in conjunction with the Artificial Immune Network (AIN) to improve bug prediction accuracy through its hyper-parameter optimization. For this purpose, seven machine learning classifiers, such as support vector machine Radial base function (SVM-RBF), K-nearest neighbor (KNN) (Minkowski metric), KNN (Euclidean metric), Naive Bayes (NB), Decision Tree (DT), Linear discriminate analysis (LDA), Random forest (RF) and adaptive boosting (AdaBoost), were used. The experiment was carried out on bug prediction dataset. The results showed that hyper-parameter optimization of machine learning classifiers, using AIN and its applications for software bug prediction, performed better than when classifiers with their default hyper-parameters were used. INDEX TERMS Artificial immune network (AIN), artificial immune system (AIS), hyper-parameter optimization, optimized artificial immune network (opt-aiNet), software bug prediction (SBP).

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“…Based on this information, one or more analysts, namely triagers, decide processing priorities and assign bug reports to software developers for correction. As shown in past studies [1][2][3][4][5][6], the severity attribute of a bug report is an important factor that influences its processing priority in the schedule of bug fixing activities that follow. When a bug report is submitted to a bug tracking system (BTS), such as Bugzilla (https: //www.bugzilla.org, accessed on 7 July 2021) or MantisBT (https://www.mantisbt.org, accessed on 7 July 2021), the reporter generally assigns a severity level to the bug report based on personal judgement.…”

Section: Introductionmentioning

confidence: 96%

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

Dao

Yang

2021

Mathematics

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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.

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Bug report severity level prediction in open source software: A survey and research opportunities

Cited by 42 publications

References 29 publications

LCBPA: An Enhanced Deep Neural Network-Oriented Bug Prioritization and Assignment Technique Using Content-Based Filtering

LCBPA: An Enhanced Deep Neural Network-Oriented Bug Prioritization and Assignment Technique Using Content-Based Filtering

Hyper-Parameter Optimization of Classifiers, Using an Artificial Immune Network and Its Application to Software Bug Prediction

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

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