Accomplishments and Challenges: A Research Study for Software Defect Prediction
Rajesh Prasad1, Sunil Kadam2, Vinayak Patil3, Pramod Jadhav4, Vinod Patil5, Amol Kadam6
1Dr. Rajesh Prasad, Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.
2Dr. Sunil Kadam, Department of Mechanical Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Kolhapur (Maharashtra), India.
3Prof. Vinayak Patil, Department of Mechanical Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Navi Mumbai (Maharashtra), India.
4Dr. Pramod Jadhav, Department of CSBS, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.
5Dr. Vinod Patil, Associate Professor, Department of Electronics and Telecommunication, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.
6Dr. Amol Kadam, Associate Professor, Department of CSBS, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.
Manuscript received on 24 April 2026 | First Revised Manuscript received on 03 May 2026 | Second Revised Manuscript received on 09 May 2026 | Manuscript Accepted on 15 May 2026 | Manuscript published on 30 May 2026 | PP: 8-14 | Volume-15 Issue-6, May 2026 | Retrieval Number: 100.1/ijitee.F127415070626 | DOI: 10.35940/ijitee.F1274.15060526
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Investigation and prediction of defects in software is one of the important solutions to ensure software quality and reliability. Machine learning algorithms are used across a wide array of fields to solve real-world problems by building large, complex models. Many researchers have made significant contributions by developing predictive models for software defects using statistical and machine-learning approaches. But only a few frameworks have discussed the issue of building a universal software defect prediction model. Most existing models have been trained on limited datasets, which results in good performance on the training data but poor performance on unseen data. These limitations have motivated researchers to explore and develop more generalised and universal models for software defect prediction. Moreover, the growing complexity of contemporary software systems. Such limitations have encouraged researchers to investigate and build more generalised and universal models for software defect prediction. In addition, the increasing complexity of modern software systems and the rapid growth of software repositories have driven a demand for intelligent prediction techniques capable of handling heterogeneous data. Research is being conducted to investigate advanced machine learning and deep learning methods, including ensemble learning and transfer learning, to enhance prediction accuracy and adaptability across different software projects. These approaches aim to reduce development and maintenance costs and increase the overall reliability and performance of software products.
Keywords: Software Defect, Cost, Class Imbalance, Data Extract
Scope of the Article: Computer Science and Engineering