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4 articles for “LLM-assisted programming”
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LLM-Assisted Programming: Trends, Evaluation Gaps, and Software Assurance
Abstract: Large language models (LLMs) are increasingly used for code generation, software testing, program repair, and security analysis. However, their evaluation still relies heavily on functional benchmarks that often focus on small, isolated programming tasks. Such benchmarks provide limited insight into security, test quality, repository-level dependencies, and maintainability. This study examines how the evaluation of LLM-assisted programming has developed across these application areas. The literature shows a gradual shift from function-level …
Published in Recent Trends in Programming languages · Vol. 13, Issue 2, 2026 · pp. 28–42 Read article
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Challenges, Risks, and Limitations of Vibe Coding in AI-Assisted Software Development
Abstract: Vibe coding is a new way of programming driven by LLMs (large language models). It helps developers write code from natural language. Developers can use prompts with artificial intelligence (AI) for code generation. AI assistants generate software directly through text. They can now perform tasks and understand user requirements. This process makes software development faster and easier for developers, but vibe coding has some drawbacks. Sometimes, it leads to security …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 · pp. 43–48 Read article
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AI-Assisted Programming, Memory Safety, Multi-Paradigm Design and Emerging Development Models
Abstract: As the field of artificial intelligence (AI) continues to evolve at a fast pace, and specifically with the rise of large language models (LLMs), the software design, implementation, testing, and maintenance landscape has undergone unprecedented transformation. Although programming languages are still the main language for writing computational logic, the way developers interact with programming languages is now increasingly mediated by AI-based tools. Current AI coding systems can help with code …
Published in Recent Trends in Programming languages · Vol. 13, Issue 2, 2026 · pp. 43–50 Read article
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VeriSci—AI-Based Multi-Modal Research Assistant
Abstract: The exponential growth of scientific literature is a major bottleneck for academic researchers who want to efficiently discover, assess, and synthesize relevant scholarly knowledge. Traditional methods of literature review are heavily reliant on manual keyword searching, human screening, and subjective data extraction, making them time-consuming, susceptible to cognitive bias, and less effective as the tidal wave of information continues to grow. To overcome these limitations, this study introduces VeriSci, an …
Published in Journal of Advanced Database Management & Systems · Vol. 13, Issue 2, 2026 · pp. 20–27 Read article