Analyzing software quality and maintainability in object-oriented systems using software metrics

Abstract

Effective evaluation of software quality and maintainability is compulsory for successful object-oriented system development, and the potential of software metrics in achieving these goals are investigated in this research. To evaluate the quality of software, this research employs software metrics to identify potential errors and weaknesses in object-oriented systems. This analysis has been conducted by us in the Python programming language. We have applied machine learning techniques to different software metrics to analyze the issues consistently, which has evaluated the effectiveness and long-term feasibility of the system. Lastly, this study establishes a foundation for future advancements in software quality assurance, demonstrating the significant benefits of integrating machine learning with traditional quality measurements to enhance the predictability and reliability of object-oriented systems.