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@article{176160,
        author = {RAJATH KRISHNA V R and ASWATH RAJ K and Vineetha Vijayan, Assistant Professor},
        title = {Software Bug Prediction Using Supervised Machine Language},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5542-5547},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176160},
        abstract = {Software bugs continue to be a major challenge in software development, often resulting in increased costs, delayed project delivery, and reduced product reliability. Early detection and prediction of bugs play a crucial role in improving software quality, minimizing risks, and streamlining the development process. This project focuses on predicting software bugs using supervised machine learning techniques by leveraging historical software metrics and defect data.
The study utilizes datasets from publicly available repositories such as PROMISE and NASA, which provide real-world software metrics and associated defect labels. A range of supervised learning algorithms including Decision Trees, Random Forests, Support Vector Machines (SVM), and Logistic Regression are implemented using Python and the Scikit-learn library. These models are trained to classify software modules as defective or non-defective based on input features derived from the datasets.},
        keywords = {},
        month = {April},
        }