Automated code review systems using CodeBERT with multi-anchor space-aware temporal convolutional neural network

Prabakar, D.

doi:10.1016/j.cola.2026.101396

Automated code review systems using CodeBERT with multi-anchor space-aware temporal convolutional neural network

2026-05-01

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DOI：10.1016/j.cola.2026.101396

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Code review represents an essential process for contemporary software development because it requires extensive time to improve code quality through bug detection and knowledge transfer. Learning-based methods which include transformer models demonstrate strong performance in automating code review because they implement multiple learning-based techniques according to studies which show positive results with CodeBERT. but they create challenges because they need to understand both structured and time-based elements of code changes to develop useful feedback for review purposes. This disconnect drives the desire to have a common structure, jointly modeling semantics and temporal code development, to fully automate the review of code. In this paper, propose an automated code review framework that integrates CodeBERT, a transformer-based pre-trained language model, with a Multi-Anchor Space-Aware Temporal Convolutional Neural Network (MASATCNN). The proposed framework is designed to address three key code review tasks in a unified manner: code change quality estimation, review comment classification and generation, and code refinement. By combining contextual semantic embeddings with space-aware temporal feature extraction, the framework effectively captures both finegrained code semantics and structural evolution patterns. Evaluate the proposed approach on multiple benchmark datasets and compare it with classical baselines and recent state-of-the-art methods, including advanced transformer-based and large language model-driven approaches. The experimental findings prove that the suggested framework is always superior to the competing methods in all the tasks with up to 4 to 6% points improvement in classification accuracy, up-to 1 point higher BLEU scores and human-rated relevance and informativeness in comment generation, and significant improvements in the accuracy of the selected refinement codes. These findings indicate the efficiency of a combination of semantic and temporal modeling to automated code review and show how the proposed framework can aid with scalable and high-quality software development processes.

Keywords:

Automated code Review

CodeBERT

Deep learning

Multi-anchor space-aware temporal convolu-tional neural network

Natural language processing

Transformer models

Automated code review systems using CodeBERT with multi-anchor space-aware temporal convolutional neural network

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