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Vibe coding is a software development practice in which developers state intent in natural language and large language models generate code. It is often framed as one-shot prompting, but the evidence describes an intent-driven, iterative workflow whose outcomes depend on how generated code is evaluated and governed. Knowledge of how vibe coding is defined, practiced, and governed is scattered across academic and practitioner sources, and, to our knowledge, existing reviews have not yet integrated both evidence streams. We conducted a multivocal literature review of peer-reviewed and grey literature following established guidelines. Searches spanned 2022 to October 2025. After screening, credibility assessment, and snowballing, 47 sources were retained (28 peer-reviewed and 19 grey) and analyzed through descriptive mapping and thematic synthesis across eight research questions. Vibe coding is consistently described as an iterative generation-evaluation-revision loop rather than a one-shot activity, and developer work shifts from writing code towards specification, supervision, and validation. Short-term productivity and time-to-prototype gains are reported in 21 of 47 sources (45%), while evidence on maintainability, long-term quality, and safeguard effectiveness remains limited. Evidence is strongest for prototyping and user-interface work and weakest for production, data-intensive, and safety-critical use, and tool visibility does not imply effectiveness. This is one of the first reviews to integrate peer-reviewed and grey literature on vibe coding under a single documented protocol. Future work should evaluate safeguard effectiveness, study session-level dynamics and long-term maintainability, and test vibe coding in production, data-intensive, and safety-critical settings.
Automated authoring of Gherkin Behavior-Driven Development (BDD) acceptance criteria remains a manual bottleneck in requirements engineering. This study investigates whether epic-organized LLM-generated Gherkin produces higher quality and coverage than requirement-aligned generation. We compare our Timeless (an epic-organized LLM pipeline) approach against a naive large language model (LLM) baseline on four requirements documents (107 requirements) from the PURE dataset. Evaluation covers structural metrics, automated requirement coverage via TF-IDF and dense embeddings, and blind expert assessment by four researchers. In our evaluation, the JSON-constrained pipeline produced structurally valid scenarios across all generated outputs, while the zero-shot baseline achieved 99% structural validity. Semantic coverage was comparable to the baseline, with Timeless achieving 94.3% semantic Requirement Coverage Rate compared with 92.9% for the baseline. TF-IDF produced lower coverage scores for the epic-organized output, suggesting that lexical metrics may miss coverage when scenarios paraphrase requirements at a higher level of abstraction. Expert raters prefer the epic-organized strategy on Correctness (4.61 vs 4.14), Executability (4.61 vs 4.07), and Completeness (4.31 vs 3.50). Overall, the results suggest that epic-organized generation can improve perceived Gherkin quality while maintaining comparable semantic coverage, although broader replication is needed before generalizing this finding.
Organisations are examining how generative AI can support their operational work and decision-making processes. This study investigates how employees in a energy company understand AI adoption and identify areas where AI and LLMs-based agentic workflows could assist daily activities. Data was collected in four weeks through sixteen semi-structured interviews across nine departments, supported by internal documents and researcher observations. The analysis identified areas where employees positioned AI as useful, including reporting work, forecasting, data handling, maintenance-related tasks, and anomaly detection. Participants also described how GenAI and LLM-based tools could be introduced through incremental steps that align with existing workflows. The study provides an overview view of AI adoption in the energy sector and offers a structured basis for identifying entry points for practical implementation and comparative research across industries.
Refactoring is a constant activity in software development and maintenance. Scale and maintain software systems are based on code refactoring. However, this process is still labor intensive, as it requires programmers to analyze the codebases in detail to avoid introducing new defects. In this research, we put forward a large language model (LLM)-based multi-agent system to automate the refactoring process on Haskell code. The objective of this research is to evaluate the effect of LLM-based agents in performing structured and semantically accurate refactoring on Haskell code. Our proposed multi-agent system based on specialized agents with distinct roles, including code analysis, refactoring execution, verification, and debugging. To test the effectiveness and practical applicability of the multi-agent system, we conducted evaluations using different open-source Haskell codebases. The results of the experiments carried out showed that the proposed LLM-based multi-agent system could average 11.03% decreased complexity in code, an improvement of 22.46% in overall code quality, and increase performance efficiency by an average of 13.27%. Furthermore, memory allocation was optimized by up to 14.57%. These results highlight the ability of LLM-based multi-agent in managing refactoring tasks targeted toward functional programming paradigms. Our findings hint that LLM-based multi-agent systems integration into the refactoring of functional programming languages can enhance maintainability and support automated development workflows.
An Evaluation Agent, middleware that combines Natural Language Inference factual verification, a five-signal poison detector with relevance-weighted aggregation, and a Trust Index is proposed, which reliably blocks instruction injection of unsafe advice while contradiction and subtle semantic weakening remain hard.