Machine learning techniques for predicting software code properties using design metrics

Abstract

This paper proposed an information technology to predict code properties based on software design metrics, underscoring the critical interplay between metrics and software code properties. A meticulous case study leveraging data from 39 open-source Java projects demonstrates the efficacy of machine learning methodologies, including random forest and artificial neural networks, in predicting code properties utilizing selected design metrics. The study reveals insights into the correlation between design metrics and lines of code (LOC), suggesting the feasibility of using design metrics for LOC forecasting and, by extension, various software characteristics. The findings emphasize the importance of prioritizing generalizability over specificity to enhance the model's reliability across diverse software engineering contexts. Overall, this paper advances our understanding of the significance of design metrics in forecasting code properties, providing valuable insights into their application within software engineering practices to mitigate risks and enhance software quality. Through these contributions, this research lays a solid foundation for further exploring and utilizing design metrics in software development processes.