Title: A systematic literature review of secure and advanced software defect prediction

Authors: Ayushmaan Pandey; Jagdeep Kaur

Addresses: Department of Computer Science and Engineering, Dr B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144008, India ' Department of Computer Science and Engineering, Dr B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144008, India

Abstract: Software defect prediction (SDP) is a critical component of software development. In this paper problems have been discussed that arise when attempting to predict software defects, including the requirement for reliable and effective data balancing and feature selection techniques to deal with the complex datasets. We examine how data security, class imbalance, and feature selection issues are related to the performance of SDP models. We have emphasised the significance of taking security concerns into account when predicting software defects. This survey paper offers insights into these primary issues of data security and reliability of SDP models and their potential solutions and a thorough analysis of the current advancements made in SDP today. After a detailed study of SDP techniques developed in the past few years, we have provided some future challenges and recommendations that may further enhance the performance of the current models.

Keywords: class imbalance; homomorphic encryption; differential privacy; feature selection; SDP; software defect prediction; federated learning.

DOI: 10.1504/IJSSE.2026.154880

International Journal of System of Systems Engineering, 2026 Vol.16 No.3, pp.336 - 360

Received: 07 Jul 2023
Accepted: 09 Oct 2023
Published online: 17 Jul 2026 *

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