Comprehensive Review: Key metrics in defect prediction Models
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Keywords:
Defect prediction models, metrics, line of code, code smellsAbstract
Defect prediction models are crucial in identifying potential issues within software systems. Numerous software quality models have been proposed and developed to assess and improve the quality of software products [1]. This article explores key metrics employed in defect prediction models, including Lines of Code, Cyclomatic Complexity, Code Churn, Code Coupling, Code Complexity Metrics, Code Smells, Test Metrics, Developer Collaboration Metrics, Historical Defect Density, Size of Changes, and Contextual Metrics. 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 [2]. However, challenges such as imbalanced datasets, evolving software projects, overfitting, context sensitivity, lack of standardization, and incorporating human factors need addressing. Evaluation metrics and validation techniques, including cross-validation and external validation, play a vital role in overcoming these challenges and improving the accuracy and applicability of defect prediction models.
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References
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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.
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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.