Defect Prediction Models in Software Engineering: A Comprehensive Review on Methodologies
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Keywords:
Software Engineering, Defect Prediction ModelsAbstract
Defect prediction models play a crucial role in software engineering by aiding in the identification and prevention of defects before they impact the software's reliability and performance. This research article provides a comprehensive review of defect prediction models, examining their evolution, methodologies, challenges, and future directions. These metrics provide quantitative insights into code quality and defect proneness. Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction [1]. The aim is to offer researchers and practitioners insights into the current state of defect prediction models and guide future advancements in this critical area of software quality assurance.
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References
A. G. Koru and H. Liu, "Building effective defect-prediction models in practice," in IEEE Software, vol. 22, no. 6, pp. 23-29, Nov.-Dec. 2005, doi: 10.1109/MS.2005.149. quality; software metrics.
Pargaonkar, S. (2020). A Review of Software Quality Models: A Comprehensive Analysis. Journal of Science & Technology, 1(1), 40–53. Retrieved from https://thesciencebrigade.com/jst/article/view/37
C. Tantithamthavorn, S. McIntosh, A. E. Hassan and K. Matsumoto, "The Impact of Automated Parameter Optimization on Defect Prediction Models," in IEEE Transactions on Software Engineering, vol. 45, no. 7, pp. 683-711, 1 July 2019, doi: 10.1109/TSE.2018.2794977.
Pargaonkar, S. “Achieving Optimal Efficiency: A Meta-Analytical Exploration of Lean Manufacturing Principles”. Journal of Science & Technology, vol. 1, no. 1, Oct. 2020, pp. 54-60, https://thesciencebrigade.com/jst/article/view/38
Pargaonkar, S. “Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering”. Journal of Science & Technology, vol. 1, no. 1, Oct. 2020, pp. 61-66, https://thesciencebrigade.com/jst/article/view/39
Ghotra, B., McIntosh, S., & Hassan, A. E. (2015, May). Revisiting the impact of classification techniques on the performance of defect prediction models. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (Vol. 1, pp. 789-800). IEEE.
Pargaonkar, S. “Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering”. Journal of Science & Technology, vol. 1, no. 1, Oct. 2020, pp. 67-81, https://thesciencebrigade.com/jst/article/view/40
S. Pal and A. Sillitti, "A Classification of Software Defect Prediction Models," 2021 International Conference "Nonlinearity, Information and Robotics" (NIR), Innopolis, Russian Federation, 2021, pp. 1-6, doi: 10.1109/NIR52917.2021.9666110.
Pargaonkar, S. “Quality and Metrics in Software Quality Engineering”. Journal of Science & Technology, vol. 2, no. 1, Mar. 2021, pp. 62-69, https://thesciencebrigade.com/jst/article/view/41
Pargaonkar, S. “The Crucial Role of Inspection in Software Quality Assurance”. Journal of Science & Technology, vol. 2, no. 1, Mar. 2021, pp. 70-77, https://thesciencebrigade.com/jst/article/view/42
Pargaonkar, S. “Unveiling the Future: Cybernetic Dynamics in Quality Assurance and Testing for Software Development”. Journal of Science & Technology, vol. 2, no. 1, Mar. 2021, pp. 78-84, https://thesciencebrigade.com/jst/article/view/43
Pargaonkar, S. “Unveiling the Challenges, A Comprehensive Review of Common Hurdles in Maintaining Software Quality”. Journal of Science & Technology, vol. 2, no. 1, Mar. 2021, pp. 85-94, https://thesciencebrigade.com/jst/article/view/44
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Copyright (c) 2021 Prof. William Turner
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License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.