Defect Prediction Models: Industry Adoption Best Practices and Case Studies
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Keywords:
Defect prediction, software quality, case studies, industry adoption, best practicesAbstract
The article discusses the industry adoption and best practices in defect prediction models, emphasizing their integration, collaboration, explain ability, continuous improvement, and alignment with organizational goals. It outlines key practices such as integration into development workflows, collaboration between data scientists and developers, explain ability for developer understanding, continuous model evaluation, addressing imbalanced datasets, context-aware model configuration, training on representative data, feedback loops for continuous improvement, rigorous model evaluation metrics, and alignment with organizational goals. 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. These practices collectively enable organizations to proactively manage software quality, improve development workflows, and deliver reliable software solutions.
In addition, the case studies presented showcase the real-world application and impact of defect prediction models in diverse industries. Numerous software quality models have been proposed and developed to assess and improve the quality of software products.The cases include a large-scale e-commerce platform achieving a 20% reduction in post-release defects, a software development consultancy improving resource allocation efficiency by 15%, an open-source software community experiencing a 30% reduction in the time taken to address defects, and a financial services organization achieving a 25% reduction in security-related defects. Lessons learned from these case studies highlight the importance of tailoring to context, continuous feedback loops, integration into workflows, metric selection, transparency, and collaboration in maximizing the effectiveness of defect prediction models across various development contexts.
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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.
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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.
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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.