Shaping the Future: Emerging Trends in Defect Prediction Models
Keywords:
Natural Language Processing, Time Series Analysis, software quality engineering, Software MaintenanceAbstract
The realm of defect prediction models is undergoing a transformative phase, marked by emerging trends that echo advancements in technology and evolving software development practices. Numerous software quality models have been proposed and developed to assess and improve the quality of software products [1]. This article explores the notable trends shaping the future of defect prediction models, including the integration of Natural Language Processing (NLP), transfer learning, automated feature engineering, ensemble learning, time series analysis, CI/CD integration, Explainable AI (XAI), edge computing, automated model hyperparameter tuning, and feedback loop mechanisms. 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]. These trends reflect the field's adaptability to the dynamic nature of software projects, promising more advanced, adaptable, and effective approaches to ensuring software quality.
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