Software Bug Prediction Using Supervised Machine Learning Algorithms
Software defects, Logistic Regression, Support Vector Machine (SVM), Random Forest (RF)
The research focuses on predicting software defects to enhance industrial success by providing measurable outcomes for development teams. Identifying defective code areas aids developers in bug pinpointing and optimizing testing efforts. Early detection depends on achieving a high percentage of accurate classification, which is crucial. Although software-defected data sets are large, they are only partially recognized and supported. In contrast to previous research that utilized the Weka simulation tool, In this paper, the machine learning techniques of Logistic Regression, Support Vector Machine (SVM), and Random Forest (RF) are proposed. The systematic analysis measures parameters like confusion, precision, recall, and recognition accuracy, comparing them to existing methods. According to the results, Random Forest had a remarkable accuracy of 98.85% while SVM had an accuracy of 97.75%. However, Logistic Regression lagged behind with 64% accuracy.
"Software Bug Prediction Using Supervised Machine Learning Algorithms", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 10, page no.358 - 364, October-2023, Available :https://ijsdr.org/papers/IJSDR2310062.pdf
Volume 8
Issue 10,
October-2023
Pages : 358 - 364
Paper Reg. ID: IJSDR_208907
Published Paper Id: IJSDR2310062
Downloads: 000347223
Research Area: Computer Science & Technology
Country: Bangalore, Karnataka, India 🇮🇳
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
