Hybrid AI Enabled Tools for Software Automation and Intelligent Code Analysis
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Automated software engineering is undergoing a paradigm shift, driven by the integration of sophisticated Artificial Intelligence (AI) techniques. This chapter explores the frontier of Hybrid AI-Enabled Tools for Software Automation and Intelligent Code Analysis. We delve into the limitations of purely statistical or symbolic AI models and present a compelling case for hybrid approaches that synergistically combine deep learning’s pattern recognition capabilities with the logical reasoning of symbolic AI. The chapter introduces a novel hybrid neuro-symbolic model designed for a suite of software automation tasks, including bug detection, code summarization, vulnerability analysis, and automated test generation. Through a comprehensive evaluation using established datasets like CodeSearchNet and Defects4J, we demonstrate the superior performance of our hybrid model over traditional machine learning baselines. The results showcase significant improvements in accuracy, explainability, and generalization across multiple programming languages. We conclude with a discussion on the practical implications of these tools for the software development lifecycle and outline future research directions in this rapidly evolving domain.
References
- Luc De Raedt et al. Statistical relational artificial intelligence: Logic, probability,and computation. Morgan & Claypool Publishers, 2016.
- Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. “Learning to represent programs with graphs”. In: arXiv preprint arXiv:1711.00740 (2017).
- Veselin Raychev, Pavol Bielik, and Martin Vechev. “Probabilistic model for code with decision trees”. In: ACM SIGPLAN Notices 51.10 (2016), pp. 731–747.
- Xiao Long et al. “Understanding and enhancing CS students’ interaction experience with AI coding assistant tools”. In: ACM Transactions on Software Engineering and Methodology (2025).
- Venkata Krishna Bharadwaj Parasaram. “Bias in Black Boxes: A Framework for Auditing Algorithmic Fairness in Financial Lending Models”. In: ().
- P Hitzler et al. “Neural-symbolic learning and reasoning: A survey and interpretation”. In: Frontiers in artificial intelligence and applications 342 (2022), pp. 1–51.
- Sahil Bhatia, Pushmeet Kohli, and Rishabh Singh. “Neuro-symbolic program corrector for introductory programming assignments”. In: Proceedings of the 40th international conference on software engineering. 2018, pp. 60–70.
- Forough Arabshahi et al. “Conversational neuro-symbolic commonsense reasoning”. In: Proceedings of the AAAI conference on artificial intelligence. Vol. 35. 6. 2021, pp. 4902–4911.
- Yutao Xie et al. “Survey of code search based on deep learning”. In: ACM Transactions on Software Engineering and Methodology 33.2 (2023), pp. 1–42.
- Matias Martinez et al. “Automatic repair of real bugs in java: A large-scale experiment on the defects4j dataset”. In: Empirical Software Engineering 22.4 (2017), pp. 1936–1964.
Principles of Hybrid Intelligent Systems
