<p>Accurate and efficient detection of software defects in complex and high-dimensional datasets is one of the biggest challenges in software development. For this purpose, the study proposes the AGT-GA-LSTM framework, which integrates gate-adjustable LSTM and the Artificial Gorilla Troops optimizer. Defect detection and management are essential at these stages to guarantee the software’s dependability and quality. This research focuses on enhancing defect detection through the integration of deep learning (DL) techniques, by proposing a novel model, the Artificial Gorilla Troops optimizer-tuned Gate Adjustable Long Short-Term Memory (AGT-GA-LSTM). Using DL to forecast software faults, this research tries to create a strong framework for defect detection. Publicly available software module datasets are utilized. Before feeding the data into the model, Z-score normalization is applied during the preprocessing phase to enhance the quality of the data. Feature extraction is performed using Kernel Principal Component Analysis (Kernel-PCA) to reduce dimensionality while retaining essential information, improving the model’s performance, and then feeding the processed data into the AGT-GA-LSTM model. This model combines the Artificial Gorilla Troops optimizer, which enhances the LSTM’s performance, with gate mechanisms to adaptively filter important features for prediction tasks. The results show that the AGT-GA-LSTM performs better than traditional machine learning models, with a 92.4% accuracy value, 91.6% precision, 93.7% recall value, and 92.9% F1-score value. The findings underscore the effectiveness of DL in defect detection, offering significant improvements in software quality assurance practices. It concludes that AGT-GA-LSTM provides a promising direction for advancing defect detection technology in software development.</p>