Aug 2026· International Conference Computational Vision and Bio Inspired Computing· pp. 419-422· 0 citations· 20 references
Abstract
Software Defect Prediction (SDP) is an important aspect of enhancing software quality by determining early on in the development lifecycle the modules that are likely to be error prone. Complex machine learning methods fail to provide hidden nonlinear relationships within complex software measures. Our study has suggested a hybrid deep learning system with convolutional neural networks (CNNs) and long short-term memory (LSTM) networks as a viable solution to software defect prediction to overcome this limitation. The data of 10,885 modules in 21 features were used to train the model on a real-world software measure dataset. The proposed CNN-LSTM architecture, following the preparation and optimization of the model, reached the test accuracy threshold of ${7 5. 1 5 \%}$, which further improved to ${8 1. 3 0 \%}$ with the addition of threshold optimization. These findings prove that the hybrid deep learning model is effective at capturing both local patterns and sequence dependencies in software metrics which substantially improves the prediction performance. The suggested solution offers an effective and dependable solution to software quality assurance and can assist developers in prioritizing testing and minimizing defects within large software systems.
A novel hybrid model based on a fine-tuned fully connected fully connected deep neural network, a random forest, and a convolutional neural network that achieves competitive fault prediction performance while requiring lower computational complexity and training time than recent deep-learning approaches.
Mehrasa Jouybari, Alireza Tajary, M. Fateh et al.· Jordanian Journal of Compute...· 0 citations
This study applies both algorithms to detect software defects using 11 open-source datasets from the PROMISE repository and indicates that CNN outperforms MLP, achieving 86% prediction accuracy and an F1 score of 87.7%.
Ekhlas Tariq Hasan, S. Mohi-Aldeen· International research journ...· 0 citations
Software development effort estimation is a critical aspect of effective project planning, as inaccurate predictions can lead to cost overruns, schedule delays, and incomplete system implementation. This study evaluates five LSTM-based deep learning architectures—Standard LSTM, CNN-BiLSTM, Residual LSTM, LSTM-GRU, and...
Santa Margita, Eko Sediyono, S. Y. J. Prasetyo et al.· HighTech and Innovation Jour...· 0 citations
The high rate of growth of advanced and highly obfuscated malware has made the use of conventional signature detection mechanisms less viable. In order to overcome this problem, this paper suggests a hybrid framework for malware classification based on the combination of Convolutional Neural Networks (CNNs) and Long Sh...
Chevella Anil Kumar, Saradha S, Mithila Ayyavoo et al.· International Research Journ...· 0 citations
: Deep learning (DL) is now routine in software defect prediction (SDP), yet how much it improves on traditional machine learning (ML), how stable that improvement is, and what governs it remain contested. We synthesized 45 empirical studies published between 2015 and 2024, comprising 1540 performance estimates, using...
Wei-Xiang Gan, Jia-Lin Liu, Meng-Fei Xiao et al.· Engineering and Computing In...· 0 citations
Digital public service applications generate large-scale user reviews that provide valuable feedback on service quality, but their unstructured nature makes continuous monitoring challenging. This study proposes a two-stage data-driven framework that integrates FastText-based Convolutional Neural Network-Bidirectional...
