Autonomous Code Review Using Large Language Models: A Hybrid Framework for Code Quality Assessment, Refactoring Recommendation, and Technical Debt Reduction
DOI:
https://doi.org/10.64137/31079911/IJMST-V2I2P103Keywords:
Large Language Models, Autonomous Code Review, Static Analysis, Retrieval-Augmented Generation, Refactoring, Technical Debt, Software Quality, Devsecops, Software MaintenanceAbstract
Autonomous code review is emerging as a critical software engineering capability because modern teams must inspect increasingly large pull requests, multi-service dependencies, and AI-generated code changes without weakening security, maintainability, or delivery speed. This paper proposes ARC-TD, a hybrid large language model framework for code quality assessment, refactoring recommendation, and technical debt reduction. Unlike purely generative reviewers that comment directly on code diffs, ARC-TD combines static analysis, repository-aware retrieval, dependency graph reasoning, risk-calibrated LLM review, test-aware validation, and human governance into a single decision pipeline. The framework represents code review as a multi-objective optimization problem in which defects, maintainability risks, refactoring safety, architectural drift, and debt repayment value are jointly estimated. The proposed methodology defines review units, retrieves local and historical context, generates structured findings, verifies proposed refactorings against rules, tests, and semantic constraints, and prioritizes debt items using severity, propagation risk, remediation cost, and business-criticality signals. The paper presents the architecture, scoring model, review workflow, governance controls, evaluation protocol, and implementation considerations for enterprise deployment. The central contribution is a research-grade design that treats LLMs not as autonomous committers, but as bounded reasoning agents whose outputs are constrained by static evidence, historical review knowledge, quality gates, and reviewer feedback. The resulting framework aims to improve review consistency, reduce low-value reviewer effort, and transform technical debt management from episodic clean-up into continuous, evidence-grounded remediation.
References
[1] Sai Krishna Gunda, “An Exploration of Adaptive Ensemble Approaches in Software Fault Detection: Balancing Accuracy and Robustness,” The First International Conference on Recent Trends in Artificial Intelligence, Cyber Security, and Embedded Systems: ICRTACES2024, Tiruchirappalli, India, vol. 3345, no. 1, 7 January 2026, https://doi.org/10.1063/5.0298093
[2] S. R. Gudi, “Monitoring and Deployment Optimization in Cloud-Native Systems: A Comparative Study Using OpenShift and Helm,” 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 792–797, Sep. 2025, doi: https://doi.org/10.1109/icimia67127.2025.11200594.
[3] S. K. Gunda, “A Scalable AI-Driven Quality Engineering Architecture for End-To-End Validation of Core Banking, API, and UAT Ecosystems,” American International Journal of Computer Science and Technology, vol. 7, no. 6, pp. 126–138, Dec. 2025, doi: https://doi.org/10.63282/3117-5481/aijcst-v7i6p113.
[4] S. R. Gudi, “Leveraging Predictive Analytics and Redis-Backed Caching to Optimize Specialty Medication Fulfillment and Pharmacy Inventory Management,” International Journal of AI, BigData, Computational and Management Studies, vol. 5, no. 3, Oct. 2024, doi: https://doi.org/10.63282/3050-9416.ijaibdcms-v5i3p116.
[5] S. K. Gunda, “Analyzing Machine Learning Techniques for Software Defect Prediction: A Comprehensive Performance Comparison,” 2024 Asian Conference on Intelligent Technologies (ACOIT), pp. 1–5, Sep. 2024, doi: https://doi.org/10.1109/acoit62457.2024.10939610.
[6] A.Tornhill, M. Borg, N. Hagatulah, and E. Söderberg, “ACE: Automated Technical Debt Remediation with Validated Large Language Model Refactorings,” Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering, pp. 1318–1324, Jun. 2025, doi: https://doi.org/10.1145/3696630.3730565.
[7] S. K. Gunda, “AI-Enhanced API Reliability Testing for Digital Banking: Improving Accuracy, Resilience, and Integrity in Financial Transaction Processing,” International Journal of Emerging Trends in Computer Science and Information Technology, vol. 6, no. 2, pp. 136–143, May 2025, doi: https://doi.org/10.63282/3050-9246.ijetcsit-v6i2p116.
[8] R. R. Thalakanti, “Optimizing Neural Network Architecture for Binary Classification Using Evolutionary Algorithms,” 2025 International Conference on Electronics and Computing, Communication Networking Automation Technologies (ICEC2NT), pp. 1–6, Sep. 2025, doi: https://doi.org/10.1109/icec2nt65402.2025.11380048.
[9] Gunda, S.K. (2026). A Hybrid Deep Learning Model for Software Fault Prediction Using CNN, LSTM, and Dense Layers. In: Bakaev, M., et al. Internet and Modern Society. IMS 2025. Communications in Computer and Information Science, vol 2672. Springer, Cham. https://doi.org/10.1007/978-3-032-05144-8_21.
[10] S. R. Gudi, “AI-Driven Fax-to-Digital Prescription Automation: A Cloud-Native Framework Using OCR, Machine Learning, and Microservices for Pharmacy Operations,” International Journal of Emerging Research in Engineering and Technology, vol. 5, no. 1, Mar. 2024, doi: https://doi.org/10.63282/3050-922x.ijeret-v5i1p113.
[11] S. K. Gunda, “Accelerating Scientific Discovery With Machine Learning and HPC-Based Simulations,” Advances in Systems Analysis, Software Engineering, and High Performance Computing, pp. 229–252, Dec. 2024, doi: https://doi.org/10.4018/978-1-6684-3795-7.ch009.
[12] Manushree Vijayvergiya et al., “AI-Assisted Assessment of Coding Practices in Modern Code Review,” arXiv (Cornell University), Jul. 2024, doi: https://doi.org/10.1145/3664646.3665664.
[13] S. K. Gunda, “Automatic Software Vulnerabilty Detection Using Code Metrics and Feature Extraction,” 2025 2nd International Conference On Multidisciplinary Research and Innovations in Engineering (MRIE), pp. 115–120, Jul. 2025, doi: https://doi.org/10.1109/mrie66930.2025.11156601.
[14] R. R. Thalakanti, “Convergence Analysis and Implementation of Linear Multistep Methods for Solving Ordinary Differential Equations,” 2025 2nd Asian Conference on Intelligent Technologies (ACOIT), pp. 1–18, Oct. 2025, doi: https://doi.org/10.1109/acoit66109.2025.11436783.
[15] S. K. Gunda, “Comparative Analysis of Machine Learning Models for Software Defect Prediction,” pp. 1–6, Oct. 2024.
[16] Srikanth Reddy Gudi, “A Comparative Analysis of Pivotal Cloud Foundry and OpenShift Cloud Platforms,” The American Journal of Applied Sciences, vol. 7, no. 7, pp. 20–29, 2025, doi: https://doi.org/10.37547/tajas/Volume07Issue07-03
[17] Y. Wang, W. Wang, Shafiq Joty, and Steven, “CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation,” Jan. 2021, doi: https://doi.org/10.18653/v1/2021.emnlp-main.685.
[18] S. Yalamati, “Reinforcement Learning for Dynamic Service Composition in Edge Networks,” 2025 4th International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pp. 1158–1163, Dec. 2025, doi: https://doi.org/10.1109/icaaic64647.2025.11330768.
[19] R. R. Thalakanti, “Enhancing Convergence in Fully Connected Neural Networks via Optimized Backpropagation,” 2025 2nd International Conference on Computing and Data Science (ICCDS), pp. 1–6, Jul. 2025, doi: https://doi.org/10.1109/iccds64403.2025.11209625.
[20] T. Raikar, “Preserving the clean core principles in SAP systems: Design strategies for integrating AI,” 2026 International Conference on Electronic Systems and Intelligent Computing (ICESIC), pp. 1036–1041, Mar. 2026, doi: https://doi.org/10.1109/icesic67389.2026.11496501.
[21] SK Gunda, SDR Yettapu, S. Bodakunti, and S.B.Bikki, “Decision Intelligence Methodology for AI-Driven Agile Software Lifecycle Governance and Architecture-Centered Project Management,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 4, 2023, doi: https://doi.org/10.63282/3050-9262.ijaidsml-v4i1p112.
[22] S. R. Gudi, “Enhancing Optical Character Recognition (OCR) Accuracy in Healthcare Prescription Processing using Artificial Neural Networks,” European Journal of Artificial Intelligence and Machine Learning, vol. 4, no. 6, pp. 1–6, Nov. 2025, doi: https://doi.org/10.24018/ejai.2025.4.6.79
[23] P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Kuttler, M. Lewis, W. tau Yih, T. Rocktaschel, S. Riedel, and D. Kiela, "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks," in Advances in Neural Information Processing Systems, vol. 33, pp. 9459-9474, 2020.
[24] Achuta Krishna Kishore Varma Alluri, “Governed Agentic AI for Salesforce CRM Platforms: A Reference Architecture for Data Grounding, Decision Intelligence, Trust Controls, and Lifecycle Reliability,” International Journal of Emerging Trends in Computer Science and Information Technology (IJETCSIT), vol. 7, no. 1, pp. 374-82, 2026, doi: https://ijetcsit.org/index.php/ijetcsit/article/view/741
[25] S. Yalamati, “Sparse Matrix Factorization for Scalable Machine Learning in Cloud Environments,” 2025 International Conference on NexGen Networks and Cybernetics (IC2NC), pp. 333–338, Dec. 2025, doi: https://doi.org/10.1109/ic2nc67409.2025.11376338.
[26] R. R. Thalakanti, “Formalizing feature model integrity: a typing system and refactoring approaches for improving software product line design,” IET Conference Proceedings, vol. 2025, no. 43, pp. 710–717, Feb. 2026, doi: https://doi.org/10.1049/icp.2025.4792.
[27] T. Raikar and V. Apelagunta, “Implementing SAP Fiori in S/4HANA Transitions: Key Guidelines, Challenges, Strategic Implications, AI Integration Recommendations,” Journal of Engineering Research and Sciences, vol. 4, no. 11, pp. 1–9, Nov. 2025, doi: https://doi.org/10.55708/js0411001.
[28] S. K. Gunda, "Fault Prediction Unveiled: Analyzing the Effectiveness of Random Forest, Logistic Regression, and KNeighbors," 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India, pp. 107-113, 2024, https://doi.org/10.1109/ICSSAS64001.2024.10760620.
[29] S. R. Gudi, “Enhancing Reliability in Java Enterprise Systems through Comparative Analysis of Automated Testing Frameworks,” International Journal of Emerging Trends in Computer Science and Information Technology, vol. 4, 2023, doi: https://doi.org/10.63282/3050-9246.ijetcsit-v4i2p115.
[30] Z. Feng et al., “CodeBERT: A Pre-Trained Model for Programming and Natural Languages,” Empirical Methods in Natural Language Processing, Feb. 2020, doi: https://doi.org/10.18653/v1/2020.findings-emnlp.139.
[31] B. Siri and Sai, “Replacing AI Agents for Backend,” INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, vol. 09, no. 06, pp. 1–8, Jun. 2025, doi: https://doi.org/10.55041/ijsrem.ncft011.
[32] A. K. K. V. Alluri, “A Systematic Study of Machine Learning Frameworks Enabling Scalable Secure and Explainable Artificial Intelligence in Salesforce CRM Platforms,” 2026 International Conference on Electronic Systems and Intelligent Computing (ICESIC), pp. 396–401, Mar. 2026, doi: https://doi.org/10.1109/icesic67389.2026.11496486.
[33] S. Yalamati, “Energy-Efficient Task Offloading in Multi-Tenant Edge Clouds,” 2026 International Conference on Electronic Systems and Intelligent Computing (ICESIC), pp. 379–384, Mar. 2026, doi: https://doi.org/10.1109/icesic67389.2026.11496473.
[34] R. R. Thalakanti, S. S. G. Bandari, and S. D. Sivva, “Federated Learning for Privacy Preserving Fraud Detection across Financial Institutions: Architecture Protocols and Operational Governance,” International Journal of Emerging Research in Engineering and Technology, vol. 5, pp. 108–114, 2024, doi: https://doi.org/10.63282/3050-922x.ijeret-v5i2p111.
[35] T. Raikar, F. Ezeugboaja, S. Bussa, H. Upadhyay, and P. Kalaru, “Ethics of AI-based supply chain optimization: a better balance between efficiency and fairness,” Future Technology, vol. 5, no. 2, pp. 281–296, May 2026, doi: https://doi.org/10.55670/fpll.futech.5.2.26.
[36] V. K. R. Mittamidi, “An Automated AI-Driven Monitoring and Observability Framework for Cloud-Based Data Pipelines by Software Defect Prediction Research,” International Journal of Multidisciplinary Evolutionary Research, vol. 5, no. 1, pp. 109–112, 2024, doi: https://doi.org/10.54660/ijmer.2024.5.1.109-112.
[37] S. K. Gunda, “The Future of Software Development and the Expanding Role of ML Models,” International Journal of Emerging Research in Engineering and Technology, vol. 4, 2023, doi: https://doi.org/10.63282/3050-922x.ijeret-v4i2p113.
[38] S. R. Gudi, “Design and Evaluation of Secure Microservices Architecture for HIPAA-Compliant Prescription Processing on AWS and OpenShift,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 5, no. 2, Jun. 2024, doi: https://doi.org/10.63282/3050-9262.ijaidsml-v5i2p116.
[39] L. Li et al., “AUGER: automatically generating review comments with pre-training models,” Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Nov. 2022, doi: https://doi.org/10.1145/3540250.3549099.
[40] S. Naik, Praneeth Aitharaju, and Sai, “AI Chatbots in Enterprise Solutions: Transforming Customer Support, Industry-Specific Challenges and Ethical Considerations,” vol. 01, no. 01, pp. 49–59, Jan. 2025, doi: https://doi.org/10.63665/gjis.v1.11.
[41] A. K. K. Varma Alluri, “Using Salesforce CRM and Deep Learning (CNN) Techniques to Improve Patient Journey Mapping and Engagement in Small and Medium Healthcare Organizations,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 6, 2025, doi: https://doi.org/10.63282/3050-9262.ijaidsml-v6i4p115.
[42] S. Yalamati, “AI-Augmented Service Fabric for Adaptive Resource Management in Cloud Environments,” 2025 5th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), pp. 963–968, Nov. 2025, doi: https://doi.org/10.1109/icuis67429.2025.11380548.
[43] S. D. Sivva, R. R. Thalakanti, S. S. G. Bandari, and S. D. R. Yettapu, “AI-Driven Decision Intelligence for Agile Software Lifecycle Governance: An Architecture-Centered Framework Integrating Machine Learning Defect Prediction and Automated Testing,” International Journal of Emerging Trends in Computer Science and Information Technology, vol. 4, pp. 167–172, 2023, doi: https://doi.org/10.63282/3050-9246.ijetcsit-v4i4p118.
[44] Manga, S. D. Sivva, and V. K. Manga, “The Adaptive Intelligence in Cloud Systems: A Unified Architecture for AI Enhanced Observability and Automated Root Cause Analysis,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 5, pp. 160–166, 2024, doi: https://doi.org/10.63282/3050-9262.ijaidsml-v5i1p115.
[45] M. Ukey, S. R. Abbidi, T. K. Kota, T. Raikar, M. Mallepati, and P. J. Adinarayana, “Digital Transformation in Healthcare: Integrating Clinical Research with Data Management Technologies,” 2026 6th International Conference on Recent Trends in Computer Science and Technology (ICRTCST), pp. 886–891, Jan. 2026, doi: https://doi.org/10.1109/icrtcst68392.2026.11545210.
[46] V. K. R. Mittamidi, “Leveraging AI and ML for Predictive Monitoring and Error Mitigation in Change Data Capture Pipelines,” International Journal of Emerging Trends in Computer Science and Information Technology, vol. 6, pp. 104–111, 2025, doi: https://doi.org/10.63282/3050-9246.ijetcsit-v6i3p116.
[47] S. K. Gunda, “Predictive Validation of Banking APIs and Transaction Workflows Using Machine Learning-Based Defect Detection Model,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 6, no. 1, pp. 284–292, Mar. 2025, doi: https://doi.org/10.63282/3050-9262.ijaidsml-v6i1p133.
[48] S. R. Gudi, “Ensuring Secure and Compliant Fax Communication: Anomaly Detection and Encryption Strategies for Data in Transit,” 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 786–791, Sep. 2025, doi: https://doi.org/10.1109/icimia67127.2025.11200537.
[49] D. Guo et al., “GraphCodeBERT: Pre-training Code Representations with Data Flow,” arXiv.org, Sep. 13, 2021. https://arxiv.org/abs/2009.08366
[50] Sai Santosh Goud Bandari, “Machine Learning (ML) based Anomaly Detection in Insurance Industries,” Journal of Information Systems Engineering and Management, vol. 10, no. 32s, pp. 13–21, Apr. 2025, doi: https://doi.org/10.52783/jisem.v10i32s.5182.
[51] A. K. K. V. Alluri and S. Barde, “AI Powered Decision Intelligence Frameworks for Predictive and Prescriptive Business Optimization in Salesforce Enterprise Platforms,” 2026 International Conference on Electronic Systems and Intelligent Computing (ICESIC), pp. 438–443, Mar. 2026, doi: https://doi.org/10.1109/icesic67389.2026.11496409.
[52] S. Yalamati, “Probabilistic Reasoning in Multi-Agent Reinforcement Learning Systems,” 2025 International Conference on NexGen Networks and Cybernetics (IC2NC), pp. 707–712, Dec. 2025, doi: https://doi.org/10.1109/ic2nc67409.2025.11376303.
[53] R. R. Thalakanti and S. S. G. Bandari, “Intelligent Continuous Integration and Delivery for Banking Systems using Machine Learning Driven Risk Detection with Real World Deployment Evaluation,” International Journal of AI, BigData, Computational and Management Studies, vol. 5, no. 4, pp. 168–175, Dec. 2024, doi: https://doi.org/10.63282/3050-9416.ijaibdcms-v5i4p118.
[54] S. D. Sivva, “An End-to-End AI-Based Systems Engineering Paradigm for Lifecycle Governance, Predictive Quality Assurance, Automation Economics, and Cybersecurity Intelligence,” Journal of Frontiers in Multidisciplinary Research, vol. 4, no. 1, pp. 600–604, 2023, doi: https://doi.org/10.54660/.jfmr.2023.4.1.600-604.
[55] T. Raikar, “High-Performance In-Memory Computing: A Research Study on SAP S/4 HANA Database Layer,” American Journal of Technology, vol. 4, no. 2, pp. 93–113, Dec. 2025, doi: https://doi.org/10.58425/ajt.v4i2.449.
[56] N. Mutyam, “Graph-Based Modeling of Service Dependencies for Predicting Failure Propagation in Distributed Systems,” International Journal of Multidisciplinary Evolutionary Research, vol. 5, no. 1, pp. 113–116, 2024, doi: https://doi.org/10.54660/ijmer.2024.5.1.113-116.
[57] V. K. R. Mittamidi, “AI/ML Powered Intelligent Root Cause Analysis and Automated Remediation for Multi System Data Integrity Issues,” International Journal of AI, BigData, Computational and Management Studies, vol. 6, pp. 133–141, 2025, doi: https://doi.org/10.63282/3050-9416.ijaibdcms-v6i4p115.
[58] GV Krishna, BD Reddy, T. Vrindaa, “EmoVision: An Intelligent Deep Learning Framework for Emotion Understanding and Mental Wellness Assistance in Human Computer Interaction,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 6, 2025, doi: https://doi.org/10.63282/3050-9262.ijaidsml-v6i4p103.
[59] S. K. Gunda, “An Intelligent AI-Driven Framework for Real-Time ATM Transaction Validation, Fraud Detection and Financial Switching Integrity,” International Journal of Emerging Research in Engineering and Technology, vol. 5, pp. 180–191, 2024, doi: https://doi.org/10.63282/3050-922x.ijeret-v5i4p119.
[60] S. R. Gudi, “Deconstructing Monoliths: A Fault-Aware Transition to Microservices with Gateway Optimization using Spring Cloud,” 2025 6th International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 815–820, Sep. 2025, doi: https://doi.org/10.1109/icesc65114.2025.11212326.
[61] C. E. Jimenez et al., “SWE-bench: Can Language Models Resolve Real-World GitHub Issues?,” arXiv.org, Oct. 10, 2023. https://arxiv.org/abs/2310.06770
[62] S. S. G. Bandari, S. D. Sivva, and R. R. Thalakanti, “Regulatory Grade Fraud Detection using Explainable Artificial Intelligence with Auditable Decision Pathways and Empirical Validation on Banking Data,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 5, pp. 139–147, 2024, doi: https://doi.org/10.63282/3050-9262.ijaidsml-v5i3p115.
[63] M. Balerao, “A Converged Artificial Intelligence Architecture for Innovation, Software Lifecycle Optimization, and Cybersecurity Risk Mitigation,” International Journal of Multidisciplinary Futuristic Development, vol. 4, no. 1, pp. 117–120, 2023, doi: https://doi.org/10.54660/ijmfd.2023.4.1.117-120.
[64] A. K. K. Varma Alluri, “Salesforce CRM Framework for Real Time DeFi Portfolio Intelligence and Customer Engagement Forecasting in Web3 Based Decentralized Finance Ecosystems Using ML Techniques,” International Journal of AI, BigData, Computational and Management Studies, vol. 6, 2025, doi: https://doi.org/10.63282/3050-9416.ijaibdcms-v6i4p111.
[65] R. R. THALAKANTI, “AI-Driven API Architectures for Multi-Cloud Enterprises: A Comparative Study of Centralized, Distributed, and Hybrid Deployment Models,” International Journal of Computer Science and Engineering Innovations, vol. 2, no. 1, pp. 60–67, Feb. 2026, doi: https://doi.org/10.64137/31079458/ijcsei-v2i1p108.
