As generative artificial intelligence (GenAI) increasingly permeates software development, Agile organizations face growing pressure to achieve continuous lifecycle compliance amid rapidly evolving regulatory demands. Classical Structuration Theory and its derivatives account for technology as either an instantiation of social structure or a medium for human agency; neither pole adequately captures GenAI’s quasi-autonomous capacity to reshape organizational practices and give rise to new regulatory obligations. This study addresses this gap by developing Triadic Structuration Theory. Following an incremental design science research (DSR) paradigm, we develop this abstract design artifact through cycle-level intermediate artifacts, with cross-cycle knowledge accumulation. Cycle 1 derives a problem backlog of GenAI-induced tensions in Agile practice. Cycle 2 conceptualizes RegOps, showing how Agile agency adapts under regulatory pressure. Cycle 3 designs an EU AI Act-aligned model card template, showing how regulatory structure becomes embedded in generative technology. Cycle 4 explores GenAI-enabled solutions to ISO-related compliance barriers in an Agile company. Together, the evidence and knowledge accumulated within and across the cycles position generative technology as a third construct rather than a medium between agency and structure. Regulatory structure is embedded directly into it; it reshapes Agile agency through quasi-autonomous action, and its use to meet existing obligations may recursively generate new ones. The study extends Structuration Theory as a middle-range theory to Agile and GenAI contexts, and demonstrates how nested DSR with intermediate artifacts can support theory development. For practitioners, it offers guidance on leveraging GenAI for compliance, cautioning that it is a structural intervention rather than a one-off fix.
Large language models (LLMs) are increasingly used in software engineering, yet empirical understanding of sustained LLM-assisted architecting remains limited. Existing work largely treats LLMs as generators of isolated artifacts rather than participants in continuous architecting processes. This paper presents an empirical study of vibe architecting, a minimally human-in-the-loop approach in which architectural structure, constraints, and behaviors emerge through continuous prompting with an LLM under minimal human guidance. Two systems, NutriCompass and MenuCal, are developed using ChatGPT and Gemini, respectively, under the same prompt sequence with minor progressive variations. The architectural artifacts produced through vibe architecting are evaluated using an artifact evaluation rubric based on correctness, completeness, and consistency. NutriCompass achieves slightly higher overall validity than MenuCal (0.86 vs. 0.83), primarily due to stronger completeness (0.91 vs. 0.78), while both systems exhibit comparable correctness and high consistency. The average validity across both cases is 0.84, indicating generally high artifact validity under the defined artifact evaluation rubric. Vibe architecting exhibits stronger results during early and intermediate architectural activities focusing on structural exploration with a clear and detailed system description. However, the study also confirms that although vibe architecting may reduce human effort, human involvement remains necessary to evaluate outputs and refine artifacts as the prompt sequence progresses, reinforcing the role of LLMs as architectural articulation aids rather than autonomous decision-makers. Broader industrial evaluation and prompt sensitivity analysis are left as future work directions.
Cross-lingual code clone detection aims to identify functionally equivalent code fragments written in different programming languages. Existing detectors often achieve high F1 scores on standard benchmarks, but these scores may be inflated by code or problem overlap and shallow lexical or structural cues. It is therefore unclear whether the detectors compare the semantics of two code fragments or exploit regularities in benchmark construction. To investigate this issue, we conduct a behavioral audit of six representative clone detectors on Java-to-X pairs from Project CodeNet. The audit combines protocols with increasingly strong isolation controls, shortcut controls, partner shuffling, and meaning-preserving and behavior-changing edits.Our results show that measured performance depends strongly on the evaluation protocol. GraphCodeBERT and UniXcoder fall from F1 scores of 0.9848 and 0.9916 under pair-random evaluation to 0.6588 and 0.6970 under problem-disjoint evaluation. Shallow Control achieves an F1 of 0.6889, showing that even problem-disjoint splits retain exploitable non-semantic cues. Partner shuffling further reveals that detectors differ in their reliance on the original cross-lingual pairing. Semantic counterfactuals show that GraphCodeBERT is stable under meaning-preserving edits but insensitive to behavioral changes, while DeepSeek-v4-flash exhibits the reverse pattern. No evaluated detector achieved both high preservation stability and high breaking rejection on this counterfactual set. Thus, neither high F1 nor clean splitting alone demonstrates cross-lingual semantic understanding. Evaluations should also test shortcut reliance, use of both code snippets, and responses to semantics-preserving and semantics-breaking edits.
