This work contributes longitudinal evidence on sustaining 4DWW in agile software development and proposes two conceptual artifacts: a lifecycle model of the 4DWW and a 4DWW survival matrix, explaining how external pressures and management rationale rollback risk.
Abstract
Context: Existing research on the four-day workweek (4DWW) has primarily examined its introduction and short-term effects, with limited understanding of its long-term survival or its interaction with agile software development. Objective: We study how a reduced-hour 4DWW is introduced, adapted, institutionalized, and sustained under changing organizational and external conditions in an agile software organization. Method: We conducted a longitudinal single-case study of a software organization operating a 32-hour, four-day week. The study draws on 15 semi-structured interviews in 2022 and 2026, analyzed using qualitative content analysis. Results: The 4DWW is better understood as an evolving arrangement than a one-off intervention. After the introduction, teams redesigned coordination, communication, meetings, agile practices, and iterations to adapt to reduced working time. Once institutionalized, the 4DWW faced ownership change, economic downturn, and market and AI pressures. Rather than reverting to five-day workweek, employees absorbed these pressures through voluntary protective adaptations, while anticipating that a rollback would harm job satisfaction, organizational commitment, and employer image. Contribution: We contribute longitudinal evidence on sustaining 4DWW in agile software development and propose two conceptual artifacts: a lifecycle model of the 4DWW and a 4DWW survival matrix, explaining how external pressures and management rationale rollback risk.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
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