# Do feelings matter? On the correlation of affects and the self-assessed productivity in software engineering

arXiv: https://arxiv.org/abs/1408.1293 · HTML: https://mlsanity.com/paper/1408.1293/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2014.

## Built on by (most cited first, 3 of 3)

- [Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers](https://mlsanity.com/paper/2310.17369/) (2023)
- [Using Voice and Biofeedback to Predict User Engagement during Product Feedback Interviews](https://mlsanity.com/paper/2104.02410/) (2021)
- [Exploring the Impact of Generative Artificial Intelligence on Software Development in the IT Sector: Preliminary Findings on Productivity, Efficiency and Job Security](https://mlsanity.com/paper/2508.16811/) (2025)

## Abstract (arXiv)

Background: software engineering research (SE) lacks theory and methodologies for addressing human aspects in software development. Development tasks are undertaken through cognitive processing activities. Affects (emotions, moods, feelings) have a linkage to cognitive processing activities and the productivity of individuals. SE research needs to incorporate affect measurements to valorize human factors and to enhance management styles. Objective: analyze the affects dimensions of valence, arousal, and dominance of software developers and their real-time correlation with their self-assessed productivity (sPR). Method: repeated measurements design with 8 participants (4 students, 4 professionals), conveniently sampled and studied individually over 90 minutes of programming. The analysis was performed by fitting a linear mixed- effects (LME) model. Results: valence and dominance are positively correlated with the sPR. The model was able to express about 38% of deviance from the sPR. Many lessons were learned when employing psychological measurements in SE and for fitting LME. Conclusion: this article demonstrates the value of applying psychological tests in SE and echoes a call to valorize the human, individualized aspects of software developers. It reports a body of knowledge about affects, their classification, their measurement, and the best practices to perform psychological measurements in SE with LME models.