Automated Bug Detection and Resolution Using Deep Learning: A New Paradigm in Software Engineering

Abstract

Software bugs represent one of the most critical challenges in software engineering, as they directly contribute to security vulnerabilities, unexpected system failures, and high operational costs. The presence of bugs in software systems can lead to severe consequences, including data breaches, financial losses, and reputational damage. Traditional bug detection techniques, which primarily rely on static and dynamic analysis, have been widely used for years. However, these conventional approaches have several limitations, including high false-positive rates, time-consuming debugging processes, and the necessity for significant human intervention. Moreover, as software systems grow in complexity, these manual and rule-based techniques struggle to scale efficiently.

Deep learning has emerged as a transformative approach in software engineering, particularly in the field of automated bug detection and resolution. Deep learning-based models leverage vast repositories of historical code, learning patterns and anomalies that indicate the presence of software bugs. Unlike traditional rule-based methods, which require explicit definitions of patterns, deep learning models can generalize and recognize complex relationships within the code. These models can analyze source code, execution logs, and software behavior to detect and classify bugs with high accuracy.

One of the key contributions of deep learning to automated debugging is its ability to significantly improve the accuracy and efficiency of bug detection. Neural networks, for instance, can process vast amounts of software code and detect hidden patterns associated with software faults. Convolutional neural networks (CNNs) have been adapted for token-based source code analysis, while recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks excel in capturing sequential dependencies in code execution. Additionally, transformer-based models like CodeBERT and GPT-4 have demonstrated exceptional performance in bug detection and resolution by leveraging large-scale training datasets.

This paper provides an in-depth analysis of the latest advancements in deep learning for bug detection and resolution. It discusses the role of various neural network architectures, including supervised and unsupervised learning models, in improving debugging processes. The study further explores how transfer learning enhances the adaptability of bug detection models by enabling pre-trained models to be fine-tuned on specific software projects. Additionally, reinforcement learning techniques have been applied to automated debugging, where models are trained to optimize their predictions and corrective actions based on real-world debugging scenarios.

Despite the remarkable progress in deep learning-based bug detection, several challenges persist. The availability of high-quality labeled datasets remains a major obstacle, as training deep learning models requires extensive and accurately labeled data. Another challenge is the interpretability of deep learning models; many neural networks function as black boxes, making it difficult to understand how a particular prediction was made. Furthermore, the computational cost associated with training and deploying deep learning models for bug detection is substantial, which can limit their widespread adoption in resource-constrained environments.

In this paper, we present a comparative analysis of existing deep learning models for bug detection, highlighting their strengths and weaknesses. By evaluating various models in terms of accuracy, false-positive rates, and computational efficiency, we provide insights into the most effective approaches for different software engineering contexts. Additionally, we propose a novel hybrid approach that integrates multiple deep learning techniques to further enhance the performance of automated bug detection and resolution systems. This hybrid model aims to combine the strengths of different architectures while addressing the limitations observed in individual models.

Overall, this study contributes to the growing body of research in AI-driven software engineering by demonstrating how deep learning is reshaping the field of automated debugging. The findings presented in this paper highlight the potential of deep learning to reduce software defects, improve software reliability, and streamline the debugging process. As AI-driven tools continue to evolve, their integration into software development pipelines will play a crucial role in advancing the efficiency and robustness of modern software systems.