Integrating AI-Driven Refactoring Tools with Human Expertise: A Java Development Perspective

Sai Bhargav Musuluri

doi:10.32628/CSEIT2410612433

Authors

Sai Bhargav Musuluri University Of Illinois, USA Author

DOI:

https://doi.org/10.32628/CSEIT2410612433

Keywords:

AI-Driven Refactoring, Java Development, Human-AI Collaboration, Code Quality Optimization, Software Maintenance Automation

Abstract

This article explores integrating AI-driven refactoring tools with human expertise in Java development environments, examining the evolution from traditional code maintenance to modern intelligent systems. The article discusses how artificial intelligence and machine learning capabilities enhance code refactoring processes while maintaining the crucial balance of human oversight. This article demonstrates the transformative impact of AI-assisted refactoring on software development practices by analyzing various implementation strategies, collaboration frameworks, and real-world case studies. The article highlights significant improvements in code quality, development efficiency, and team productivity by systematically applying AI-driven tools. Additionally, it examines the challenges and solutions in implementing these systems, particularly focusing on preserving business logic and managing technical debt. The article provides comprehensive insights into best practices for tool adoption, workflow integration, and quality control mechanisms while also exploring future trends in AI-driven development and their potential impact on software engineering practices.

Downloads

Download data is not yet available.

References

Alejandra Garrido, Jose Meseguer, "Formal Specification and Verification of Java Refactorings," 2006 Sixth IEEE International Workshop on Source Code Analysis and Manipulation. https://ieeexplore.ieee.org/abstract/document/4026866 DOI: https://doi.org/10.1109/SCAM.2006.16

Genggeng Liu, Chuanshumin Hu, "Refactoring Java Code for Automatic API Generation," 2018 International Conference on Cloud Computing, Big Data and Blockchain (ICCBB). https://ieeexplore.ieee.org/document/8756413

Jason Lecerf; John Brant et al., "A Reflexive and Automated Approach to Syntactic Pattern Matching in Code Transformations," 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME). https://ieeexplore.ieee.org/document/8530049 DOI: https://doi.org/10.1109/ICSME.2018.00052

Maurício Aniche, "The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software Refactoring," IEEE Transactions on Software Engineering, 2022. https://ieeexplore.ieee.org/document/9186715 DOI: https://doi.org/10.1109/TSE.2020.3021736

Wang, D., et al., "From Human-Human Collaboration to Human-AI Collaboration: Designing AI Systems That Can Work Together with People," CHI '20 Extended Abstracts, ACM, 2020. https://www.semanticscholar.org/paper/From-Human-Human-Collaboration-to-Human-AI-AI-That-Wang-Churchill/660a2244efa8af0b77fd314a1c75dcc01aa677fe

Tse, E., "Planning and Decision Making Processes," 1982 American Control Conference, IEEE, 1982.https://ieeexplore.ieee.org/abstract/document/4787992 DOI: https://doi.org/10.23919/ACC.1982.4787992

Xin Mou, Hasan M. Jamil et al., "VisFlow: A Visual Database Integration and Workflow Querying System," 2017 IEEE 33rd International Conference on Data Engineering (ICDE). https://ieeexplore.ieee.org/document/7930101 DOI: https://doi.org/10.1109/ICDE.2017.204

Gu Wei, Yin X, "The research of integration of Workflow and Services based on SOA," 2011 International Conference on E-Business and E-Government (ICEE). https://ieeexplore.ieee.org/abstract/document/5881879 DOI: https://doi.org/10.1109/ICEBEG.2011.5881879

Abdullah Almogahed et al., "Revisiting Scenarios of Using Refactoring Techniques to Improve Software Systems Quality," IEEE Access, 2022. https://ieeexplore.ieee.org/document/9932613 DOI: https://doi.org/10.1109/ACCESS.2022.3218007

Francesca Arcelli Fontana et al., "Antipattern and Code Smell False Positives: Preliminary Conceptualization and Classification," 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER). https://ieeexplore.ieee.org/document/7476682

Rohan Sharma , "AI KPIs and OKRs: Measuring Success and Maximizing Impact," SpringerLink. https://link.springer.com/chapter/10.1007/979-8-8688-0796-1_13

Christian Mühlroth; Michael Grottke, "Artificial Intelligence in Innovation: How to Spot Emerging Trends and Technologies," IEEE Transactions on Engineering Management. https://ieeexplore.ieee.org/document/9102438/citations#citations