Towards a Theory of Software Developer Job Satisfaction and Perceived Productivity
Abstract
Developer satisfaction and work productivity are important considerations for software companies. Enhanced developer satisfaction may improve the attraction, retention and health of employees, while higher productivity should reduce costs and increase customer satisfaction through faster software improvements. Many researchers and companies assume that perceived productivity and job satisfaction are related and may be used as proxies for one another, but these claims are a current topic of debate. There are also many social and technical factors that may impact satisfaction and productivity, but which factors have the most impact is not clear, especially for specific development contexts. Through our research, we developed a theory articulating a bi-directional relationship between software developer job satisfaction and perceived productivity, and identified what additional social and technical factors, challenges and work context variables influence this relationship. The constructs and relationships in our theory were derived in part from related literature in software engineering and knowledge work, and we validated and extended these concepts through a rigorously designed survey instrument. We instantiate our theory with a large software company, which suggests a number of propositions about the relative impact of various factors and challenges on developer satisfaction and perceived productivity. Our survey instrument and analysis approach can be applied to other develo…
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The job satisfaction–job performance relationship: A qualitative and quantitative review.
Judge, T. A., Thoresen, C. J., Bono, J. E., & Patton, G. K. (2001) · Psychological Bulletin · doi:10.1037/0033-2909.127.3.376 · Cited by 4,760 · see citing works
Software developers' perceptions of productivity
Meyer, A. N., Fritz, T., Murphy, G. C., & Zimmermann, T. (2014) · Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering · doi:10.1145/2635868.2635892 · Cited by 224 · cited 2× · see citing works
“Using a different lens, Meyer et al. [6] looked at how developers perceive and think about their own productivity.”
BackgroundSelf-citationWhat Predicts Software Developers’ Productivity?
Murphy-Hill, E., Jaspan, C., Sadowski, C., Shepherd, D., Phillips, M., Winter, C., Knight, A., Smith, E., & Jorde, M. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2900308 · Cited by 100 · see citing works
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The SPACE of Developer Productivity: There's more to it than you think.
Forsgren, N., Storey, M.-A., Maddila, C., Zimmermann, T., Houck, B., & Butler, J. (2021) · Queue · doi:10.1145/3454122.3454124 · Cited by 164 · see citing works
Self-citationMeasuring GitHub Copilot's Impact on Productivity
Ziegler, A., Kalliamvakou, E., Li, X. A., Rice, A., Rifkin, D., Simister, S., Sittampalam, G., & Aftandilian, E. (2024) · Communications of the ACM · doi:10.1145/3633453 · Cited by 124 · see citing works
Self-citationSignificant Productivity Gains through Programming with Large Language Models
Weber, T., Brandmaier, M., Schmidt, A., & Mayer, S. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3661145 · Cited by 64 · see citing works
“Beyond these code-centric metrics, Storey et al. [56] emphasizes the importance of developer satisfaction, and Meyer et al. [31] emphasize the focus on developers’ subjective perception.”
An Actionable Framework for Understanding and Improving Developer Experience
Greiler, M., Storey, M.-A., & Noda, A. (2023) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3175660 · Cited by 57 · see citing works
Self-citationCheng, R., Wang, R., Zimmermann, T., & Ford, D. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3651990 · Cited by 47 · see citing works
“Many strive to help developers form and maintain an appropriate level of trust in AI code generation tools—an insufficient level of trust can prevent developers from using AI to increase productivity [42, 48], while too much trust can lead to overlooking risks and safety vulnerabilities to software used by millions of…”
BackgroundSelf-citationThe SPACE of developer productivity
Forsgren, N., Storey, M.-A., Maddila, C., Zimmermann, T., Houck, B., & Butler, J. (2021) · Communications of the ACM · doi:10.1145/3453928 · Cited by 21 · see citing works
“There is also agreement that developer productivity is necessary not just to improve engineering outcomes, but also to ensure the well-being and satisfaction of developers, as productivity and satisfaction are intricately connected.(12,20) Ensuring the efficient development of software systems and the well-being of de…”
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- Authors
- Storey, M.-A., Zimmermann, T., Bird, C., Czerwonka, J., Murphy, B., & Kalliamvakou, E.
- Affiliations
- University of Victoria (CA)
- Microsoft (United States) (US)
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2021-10-01
- Volume
- 47
- Issue
- 10
- Pages
- 2125-2142
- ISSN
- 0098-5589 (ISSN-L)
- 1939-3520
- 2326-3881
- OpenAlex
- W2976409997
- Cited by
- 90
- References
- 3
- Citations by year
- 2026: 17 · 2025: 17 · 2024: 21 · 2023: 8 · 2022: 12 · 2021: 11 · 2020: 4
- FWCI
- 8.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Software Engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Job Satisfaction and Organizational Behavior
- Social Sciences › Human Factors and Ergonomics › Employee Performance and Management
- Keywords
- perceived productivity, labor productivity, developer retention, knowledge work, software development teams
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