Unhappy Developers: Bad for Themselves, Bad for Process, and Bad for Software Product
January 11, 2017 ยท Declared Dead ยท ๐ 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Daniel Graziotin, Fabian Fagerholm, Xiaofeng Wang, Pekka Abrahamsson
arXiv ID
1701.02952
Category
cs.SE: Software Engineering
Cross-listed
cs.CY
Citations
49
Venue
2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C)
Last Checked
3 months ago
Abstract
Recent research in software engineering supports the "happy-productive" thesis, and the desire of flourishing happiness among programmers is often expressed by industry practitioners. Recent literature has suggested that a cost-effective way to foster happiness and productivity among workers could be to limit unhappiness of developers due to its negative impact. However, possible negative effects of unhappiness are still largely unknown in the software development context. In this paper, we present the first results from a study exploring the consequences of the unhappy developers. Using qualitative data analysis of the survey responses given by 181 participants, we identified 49 potential consequences of unhappiness while developing software. These results have several implications. While raising the awareness of the role of moods, emotions and feelings in software development, we foresee that our classification scheme will spawn new happiness studies linking causes and effects, and it can act as a guideline for developers and managers to foster happiness at work.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Software Engineering
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
GraphCodeBERT: Pre-training Code Representations with Data Flow
R.I.P.
๐ป
Ghosted
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
R.I.P.
๐ป
Ghosted
Microservices: yesterday, today, and tomorrow
R.I.P.
๐ป
Ghosted
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
R.I.P.
๐ป
Ghosted
A Survey of Machine Learning for Big Code and Naturalness
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted