Open Access

ARTICLE

SESDP: A Sentiment Analysis-Driven Approach for Enhancing Software Product Security by Identifying Defects through Social Media Reviews

Farah Mohammad^1,2,*, Saad Al-Ahmadi³, Jalal Al-Muhtadi^1,3

1 Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh, 11543, Saudi Arabia
2 Department of Computer Science, and Technology, Arab East Colleges, Riyadh, 11583, Saudi Arabia
3 College of Computer & Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

* Corresponding Author: Farah Mohammad. Email:

Computers, Materials & Continua 2025, 83(1), 1327-1345. https://doi.org/10.32604/cmc.2025.060228

Received 27 October 2024; Accepted 05 February 2025; Issue published 26 March 2025

Abstract

Software defect prediction is a critical component in maintaining software quality, enabling early identification and resolution of issues that could lead to system failures and significant financial losses. With the increasing reliance on user-generated content, social media reviews have emerged as a valuable source of real-time feedback, offering insights into potential software defects that traditional testing methods may overlook. However, existing models face challenges like handling imbalanced data, high computational complexity, and insufficient integration of contextual information from these reviews. To overcome these limitations, this paper introduces the SESDP (Sentiment Analysis-Based Early Software Defect Prediction) model. SESDP employs a Transformer-Based Multi-Task Learning approach using Robustly Optimized Bidirectional Encoder Representations from Transformers Approach (RoBERTa) to simultaneously perform sentiment analysis and defect prediction. By integrating text embedding extraction, sentiment score computation, and feature fusion, the model effectively captures both the contextual nuances and sentiment expressed in user reviews. Experimental results show that SESDP achieves superior performance with an accuracy of 96.37%, precision of 94.7%, and recall of 95.4%, particularly excelling in handling imbalanced datasets compared to baseline models. This approach offers a scalable and efficient solution for early software defect detection, enhancing proactive software quality assurance.

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Keywords

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