Aug 2026· International Conference on Automated Software Engineering· Vol 33· 0 citations· 63 references
TL;DR
This survey synthesises research from 2020 to 2026 on AI-driven techniques for code review and software quality evaluation, focusing on two complementary paradigms: Automated Machine Learning (AutoML) and Large Language Models (LLMs).
Current research is summarized to identify key gaps and future directions to optimize LLM based APR are proposed, to assure its reliability and scalability in real world software development.
Fatmaelzahra Hamdi, Ramadam Moawad, A. Mohsen· Journal of universal compute...· 0 citations
This paper presents an empirical evaluation of Large Language Models (LLMs) for automated model-based test generation, compared with a state-of-the-art model-based testing tool (GraphWalker) and its built-in algorithms (random and quick random for edge and vertex coverage settings).
H. Şanli, Onur Kilinççeker, Cihat Çetinkaya· 0 citations
A structured scoping survey organized around the question of what decision a test changes is presented, and distinguishes the Red--Green--Refactor cycle from test-conditioned generation, execution-guided refinement, test-mediated analysis, and evaluation-only testing.
Yun-Hao Liang, Cheng-Guang Gan, Rui-Xuan Ying et al.· 0 citations
The study highlights the potential of intelligent, data-driven approaches to enhance software quality and support continuous code improvement by leveraging code metrics, syntactic and semantic features, and historical refactoring data.
Emily Johnson· International Journal of Mac...· 0 citations
The proposed approach considers code review not merely as a process of generating comments, but as a context-aware multi-stage process that includes code change assessment, risk identification, problem localization, and generation of explainable recommendations.
T. Todua, Giorgi Tsitlidze· Computational and Applied Sc...· 0 citations
LLMSuite is proposed, a hybrid test generation framework that integrates self-refinement prompting with class-level LLM reasoning into the search-based testing process and complements manually written test suites by exercising domain-specific behaviors that are often left untested.
Amirhossein Deljouyi, Annibale Panichella, Andy Zaidman· 0 citations
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