2026-10-05T21:32:54Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:28037 2026-10-05T06:44:05Z ddc:004 open_access

Beyond Individual Prompting Efficiency: A Socio-Technical Perspective on Computational Efficiency in AI-Assisted Software Engineering Smite, Darja Wivestad, Viggo Tellefsen O'Brien, Gabrielle Santos, Italo Destefanis, Giuseppe Baldassarre, Maria Teresa Lungu, Mircea Paja, Elda de Souza Santos, Ronnie AI-assisted software engineering Computational waste Team practices Mental models Empirical software engineering Vision Research agenda As Artificial Intelligence (AI) assistants and agents become increasingly integrated into software engineering work, concerns about the economic and environmental costs of AI usage continue to grow. Current discussions of computational efficiency focus primarily on model architectures, hardware optimization, and inference cost reduction. In this vision paper, we argue that computational efficiency should also be understood as a socio-technical phenomenon shaped by patterns of human-AI interaction within organizational settings. Motivated by ongoing dialogues with industry partners and informed by the Theory of Planned Behavior and Social Cognitive Theory, we introduce the concept of computational behavior to describe patterns of AI-assisted work that influence how computational resources are consumed, reused, coordinated, and amplified across software engineering activities. Building on this perspective, we propose a multilevel conceptual framework linking individual computational behaviors to emergent collective computational outcomes. We argue that individually rational AI usage behaviors may accumulate into either computational waste or collective computational efficiency depending on how interactions are shared, reused, and aligned across teams and workflows. While fragmented AI use may generate duplicated prompting, redundant generation, and coordination overhead, coordinated collective practices, such as reusable prompts, shared contextualization, and workflow integration may improve collective computational efficiency. We conclude by outlining a research agenda for studying computational coordination, including empirical and organizational AI governance in AI-assisted software engineering. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Darja Smite and Viggo Tellefsen Wivestad and Gabrielle O'Brien and Italo Santos and Giuseppe Destefanis and Maria Teresa Baldassarre and Mircea Lungu and Elda Paja and Ronnie de Souza Santos 2026 Is Part Of LIPIcs, Volume 394, 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ESEM.2026.69 urn:nbn:de:0030-drops-280377 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESEM.2026.69 eng https://creativecommons.org/licenses/by/4.0/legalcode