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@article{193766,
        author = {Mrs.Shaik Shameen Taz and M Himanth and B Govardhana Reddy and T Akash Reddy and M Jithendra Kumar},
        title = {An ML Approach for Software Defect Prediction Using JM1 Dataset},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1470-1478},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193766},
        abstract = {Ensuring the quality of software systems is essential for their effective use in complex development processes. A key part of this involves finding and predicting potential defects or issues in software components early on. This study uses the NASA-curated JM1 dataset to assess how well different machine learning techniques, such as Naïve Bayes, Decision Trees, Random Forest, Support Vector Machine, Logistic Regression, Artificial Neural Networks, and K-Nearest Neighbors, can detect software defects. The research highlights the significance of early defect detection in software development using machine learning. The experimental results indicate that the Random Forest model performs best, achieving high accuracy (81%), precision, recall, and low Root-Mean-Square Error. Furthermore, the study compares this method with previous work, offering useful insights into its performance and effectiveness. The paper concludes with possible directions for further investigation and improvement in this field.},
        keywords = {Software Defect Prediction, Machine Learning, Random Forest, JM1 Dataset, NASA MDP, Ensemble Learning.},
        month = {March},
        }