Azmain, Md. AquibTasim, FariaFerdoush, Farib Md.Khan, SalequzzamanIslam, MahdiHaque, Fatema2025-02-182025-02-1820242024-10ID 24341110ID 24341120ID 20101330ID 20101326ID 20101415http://hdl.handle.net/10361/25438Cataloged from PDF version of thesis.Includes bibliographical references (pages 45-47).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.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.57 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Lines of codeLOCCyclomatic complexitySoftware qualityQuality evaluationObject-oriented systemSoftware metricsComputer software--Quality control.Software maintenance.Object-oriented programming (Computer science).Software measurement.Thesis