International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Predicting Software Defects with Smart Ensemble Learning Techniques

B. RupadeviAssociate Professor, Dept. of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, AP, IndiaRatakonda ChandanaPost Graduate, Dept. of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, AP, India

Vol 9 No 2025 (2025): Volume 9, Special Issue of ICCIS-2025 May 2025 | Pages: 117-123

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 11-06-2025

doi Logo doi.org/10.47001/IRJIET/2025.ICCIS-202519

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Abstract

Predicting software defects is a crucial aspect of ensuring software quality, aiming to detect potential problems early in the development cycle. This paper introduces an intelligent ensemble-based machine learning approach designed to classify software modules as defective or not. The prediction model leverages static code metrics—including Lines of Code, Cyclomatic Complexity, Coupling, and Inheritance Depth—to generate accurate results. A user-friendly interface, built within a Flask web application, allows users to input data manually or upload datasets for analysis. To support developers and testers, the system delivers clear classification outcomes along with insightful recommendations. By integrating multiple classifiers, the ensemble model enhances prediction accuracy, consistency, and robustness. This work highlights the practical application of artificial intelligence in software engineering and lays the groundwork for future advancements in automated defect detection.

Keywords

Software Defect Prediction, Ensemble Learning, Machine Learning, Static Code Metrics, Software Quality Assurance


Citation of this Article

B. Rupadevi, & Ratakonda Chandana. (2025). Predicting Software Defects with Smart Ensemble Learning Techniques. In proceeding of Second International Conference on Computing and Intelligent Systems (ICCIS-2025), published in IRJIET, Volume 9, Special Issue ICCIS-2025, pp 117-123. Article DOI https://doi.org/10.47001/IRJIET/2025.ICCIS-202519

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