Developer productivity and AIEvidence check: 3.5/5
Meyer, A. N., Fritz, T., Murphy, G. C., & Zimmermann, T. (2014). Software developers' perceptions of productivity. Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2014).
Plain-English take: Developers perceive days as productive when completing many or large tasks without significant interruptions/context switches, although substantial task/activity switching can coexist with feeling productive. Founder-use interpretation: Useful for product/engineering workflow hypotheses and user research, not proof that a tool improves delivery metrics; pair perceived productivity with cycle time, quality, and wellbeing measures.
Question: How do professional software developers perceive productive and unproductive work?
Design: Peer-reviewed empirical study or research synthesis. Two studies, including a survey and an observational study, examining professional developers' perceptions of productive and unproductive work.
Developers perceive days as productive when completing many or large tasks without significant interruptions/context switches, although substantial task/activity switching can coexist with feeling productive.
Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2014) · 201410.1145/2635868.2635892Publisher abstract or article landing page captured; full-text access not established.
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Developer productivity and AIEvidence check: 4.5/5
Peng, S. & Demirer, M. (2026). The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers. Management Science (Articles in Advance).
Plain-English take: Pooling the experiments, AI access increased completed tasks by 26.08% (SE 10.3%); less-experienced developers adopted more and had larger gains, while individual experiments were noisy and heterogeneous. Founder-use interpretation: Strong evidence for testing AI-assisted engineering productivity, not a promise of proportional headcount or SaaS growth; run a controlled rollout with quality, security, rework, and customer outcomes.
Question: What effect did access to generative AI have on software developers’ task completion in three company field experiments?
Design: Peer-reviewed empirical study or research synthesis. Three randomized field experiments run in ordinary business operations at Microsoft, Accenture, and an anonymous Fortune 100 company; 4,867 software developers received randomized access or no access to an AI coding assistant.
Pooling the experiments, AI access increased completed tasks by 26.08% (SE 10.3%); less-experienced developers adopted more and had larger gains, while individual experiments were noisy and heterogeneous.
Management Science (Articles in Advance) · 202610.1287/mnsc.2025.00535Publisher abstract or article landing page captured; full-text access not established.
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