PREDICTIVE SOFTWARE QUALITY ANALYSIS USING TARGETED METRICS AND MACHINE LEARNING MODELS - Singh, Rakhi; Bansal, Mamta (ShodhKosh: Journal of Visual and Performing Arts, 2024)
Our study suggests a complete way to check the quality of software by using advanced machine learning methods on factors that were carefully chosen from large code sources. To find the factors that can best predict the future, our method focusses on important software measures such as flaw density, code churn, test coverage, cyclomatic complexity, and maintainability indices. In tests using 75 open-source projects with more than 1.2 million lines of code, we found that using selected parameters improves classification accuracy by 26% compared to models learnt on full feature sets that have not been filtered. To lower the number of dimensions, we used feature sorting and association analysis. This showed that only 20% of the original metrics have a big effect on quality forecasts, which greatly reduces overfitting and processing load. Random Forest, XGBoost, Support Vector Machines, LightGBM, and a shallow Neural Network were the five machine learning models that were tried. Random Forest had the best F1-score of 0.88, beating XGBoost by 14% and showing 92% reliability in cross-validation scenes. AUC values of 0.91 across a wide range of project areas show strong generalisability. Additionally, fine-tuning hyperparameters cut model training time by 30%. You can see that selected parameter models are better than standard methods using statistical significance tests (p < 0.01). This shows how important focused feature engineering is for getting the most accurate predictions. As shown by the 0.78 mean correlation coefficient between chosen measures and final quality scores, our research shows that focussing on a simplified parameter group not only saves computing resources but also makes things easier to understand. According to these results, real-time, data-driven quality review can be easily added to current DevOps processes, making them scalable and strong. Ensemble-based interpretability methods and real-time anomaly spotting will be studied in more detail in the future. This will pave the way for proactive quality assurance measures in software development settings that change over time.