Clustering-based association rule mining for bug assignee prediction

Sharma, Meera; Singh, Vir

doi:10.1504/ijbidm.2016.081606

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…We have observed that in all clusters, if the data size increases, rules redundancy also increases and taking of attributes as severity, priority, com-ponent and operating system as antecedents to predict the assignee, redundancy of rules decreases when compared with the previous work. 31 In this paper, our con-tribution is to reduce the redundant rules by taking two new attributes, namely component and operating system.…”

Section: Discussionmentioning

confidence: 99%

Reduction of Redundant Rules in Association Rule Mining-Based Bug Assignment

Sharma

Tandon

Kumari

et al. 2017

Int. J. Rel. Qual. Saf. Eng.

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Bug triaging is a process to decide what to do with newly coming bug reports. In this paper, we have mined association rules for the prediction of bug assignee of a newly reported bug using diff erent bug attributes, namely, severity, priority, component and operating system. To deal with the problem of large data sets, we have taken subsets of data set by dividing the large data set using Kmeans clustering algorithm. We have used an Apriori algorithm in MATLAB to generate association rules. We have extracted the association rules for top 5 assignees in each cluster. The proposed method has been empirically validated on 14,696 bug reports of Mozilla open source software project, namely, Seamonkey, Firefox and Bugzilla. In our approach, we observe that taking on these attributes (severity, priority, component and operating system) as antecedents, essential rules are more than redundant rules, whereas in [M. Sharma and V. B. Singh, Clustering-based association rule mining for bug assignee prediction, Int. J. Business Intell. Data Mining 11(2) (2017) 130-150.] essential rules are less than redundant rules in every cluster. The proposed method provides an improvement over the existing tech-niques for bug assignment problem.

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Section: Discussionmentioning

confidence: 99%

Reduction of Redundant Rules in Association Rule Mining-Based Bug Assignment

Sharma

Tandon

Kumari

et al. 2017

Int. J. Rel. Qual. Saf. Eng.

Self Cite

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show abstract

“…[20] introduced a rule-based approach and SVM, showcasing improved accuracy. [21]implemented Association Rule with Clustering, yielding higher confidence rules. [22] conducted a Comparative Study, revealing Information Retrieval-based techniques outperforming Machine Learning techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Automating Bug Triage: Unleashing the Power of Machine Learning and Natural Language Processing

Kumari,

Ahmed,

Raghuwanshi

et al. 2024

Preprint

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This paper explores the potential of automating bug triage using machine learning and natural language processing. In the context of growing software complexity, manual bug handling faces challenges leading to delays. The study critically examines traditional bug triage methods, emphasizing their limitations and the need for automated solutions. By reviewing various machine learning techniques applied in bug triage, the paper provides insights for practitioners. The proposed Bug Triage model, integrating Word2Vec embeddings and XGBoost, shows promising results in categorizing and prioritizing bug reports. The paper concludes by highlighting future research opportunities to advance bug triage methodologies and improve software development practices.

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Developing Prediction Models to Assist Software Developers and Support Managers

Sharma

Tondon

2017

Computational Science and Its Applications – ICCSA 2017

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Clustering-based association rule mining for bug assignee prediction

Cited by 4 publications

References 36 publications

Reduction of Redundant Rules in Association Rule Mining-Based Bug Assignment

Reduction of Redundant Rules in Association Rule Mining-Based Bug Assignment

Automating Bug Triage: Unleashing the Power of Machine Learning and Natural Language Processing

Developing Prediction Models to Assist Software Developers and Support Managers

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