Emotions are part of our everyday life and are known to impact cognitive skills, thus influencing job performance. This is true also for software development, an intellectual activity requiring creativity and problem-solving skills that are known to be influenced by affective states. In particular, early recognition of negative emotions, such as stress or frustration can enable just-in-time intervention for developers and team managers, in order to prevent burnout and undesired turnover. In this talk, I will provide an overview of recent research findings on developers’ emotions and their relationship with self-assessed productivity. Next, I will argue in favor of the emergence of tools to support developers’ emotion awareness at the individual and team level to improve productivity, resilience to failures and wellbeing.

1. A Journey Into the
Emotions of Software Developers
Keynote for CHASE 2024 - April 14, 2024
@NicoleNovielli nicole.novielli@uniba.it
Nicole Novielli
University of Bari, Italy
Collaborative Development Group

2. Acknowledgements
Daniela Girardi
University of Bari
Daniela Grassi
University of Bari
D. Girardi, F. Lanubile, N. Novielli and A. Serebrenik, "Emotions and Perceived Productivity of Software Developers at the Workplace, in IEEE Transactions on Software
Engineering, 2022 doi: 10.1109/TSE.2021.3087906.
D. Grassi, F. Lanubile, N. Novielli and A. Serebrenik, "Towards Supporting Emotion Awareness in Retrospective Meetings," 2023 IEEE/ACM 45th International Conference on
Software Engineering: New Ideas and Emerging Results (ICSE-NIER), Melbourne, Australia, 2023, pp. 101-105, doi: 10.1109/ICSE-NIER58687.2023.00024.
D. Girardi, N.Novielli, D. Fucci, F. Lanubile. “Recognizing Developers’ Emotions while Programming“. In Proceedings of the 42th International Conference on Software
Engineering (ICSE 2020) October, 2020 – DOI: https://doi.org/10.1145/3377811.3380374
Filippo Lanubile
University of Bari
Alexander Serebrenik
TU/e

3. Why Developers’ Emotions?

4. Ekman, P. (1999). Basic emotions. In T. Dalgleish & M. J. Power (Eds.), Handbook of cognition and emotion (pp. 45–60). John Wiley & Sons Ltd. https://doi.org/10.1002/0470013494.ch3

5. Ekman, P. (1999). Basic emotions. In T. Dalgleish & M. J. Power (Eds.), Handbook of cognition and emotion (pp. 45–60). John Wiley & Sons Ltd. https://doi.org/10.1002/0470013494.ch3
Anger Fear Disgust Surprise Joy Sadness
Ekman’s Basic Emotions

6. Picture: https://theyellowspot.com/blogs/managing-change-part-2/
Emotions influence
creativity and
problem solving
T. M. Amabile, S. G. Barsade, J. S. Mueller, and B. M. Staw,
“Affect and creativity at work,” Administ. Sci. Quart., vol. 50, no. 3, pp. 367–403, 2005.

7. Happy developers solve
problems better

8. Happy developers solve
problems better

9. Emotional Awareness
Developer
Regulate own behavior
Improve individual well-
being and productivity

11. Mapping code behavior to cause,
programming tools,
fear of failure,
unavailability of resources

12. Mapping code behavior to cause,
programming tools,
fear of failure,
unavailability of resources
Anger as a proxy for different problems
Towards SELF
Towards OTHERS
Towards OBJECT

13. Mapping code behavior to cause,
programming tools,
fear of failure,
unavailability of resources
Anger as a proxy for different problems
Towards SELF
Towards OTHERS
Towards OBJECT
Actionable insights!

14. • Programmers cooperate, directly or indirectly
• Massive adoption of social media and rise of the ‘social programmer’ (Storey, ‘12)
and the surrounding ecosystem
Software development involves interaction

15. • Programmers cooperate, directly or indirectly
• Massive adoption of social media and rise of the ‘social programmer’ (Storey, ‘12)
and the surrounding ecosystem
Software development involves interaction

16. Emotional Awareness
Developer
Regulate own behavior
Improve individual well-
being and productivity
Team
Enhance communication,
coordination
Improve productivity and
organization of work

17. Emotional Awareness
Developer
Regulate own behavior
Improve individual well-
being and productivity
Team
Enhance communication,
coordination
Improve productivity and
organization of work
Organization
Improve job satisfaction
Reduce the risk of
undesired turnover

18. Emotional Awareness
• Investigate the relationship between emotions and productivity
• Identify causes for positive and negative emotions
• Design and validate tools and practices to support emotional
awareness

19. Emotions and Self-assessed Productivity

20. Developers’ emotions, productivity and
satisfaction

21. Five companies
Dutch software development companies,
including
- One startup (1 founder and 2 employees)
- Two SMEs (between 20 and 200
employees)
- Two large companies (> 20.000 employees)
21 professional developers
- 18 men, 3 women
- Average age: 33 years
± 7.2, ranging from 23 to 50
- Average experience in software
development: 8 years
± 6.2, ranging from 1 to 25
Field Study

22. Five companies
Dutch software development companies, including
- One startup (1 founder and 2 employees)
- Two SMEs (between 20 and 200 employees)
- Two large companies (> 20.000 employees)
21 professional developers
- 18 men, 3 women
- Average age: 33 years
- Average experience in software development: 8 years
Field Study

23. RQ1. What is the range of developers’ emotions at the workplace?
RQ2. To what extent are developers emotions related to self-assessed
productivity during the workday?

24. Study protocol 2/3 weeks

25. J. A. Russell, A circumplex model of affect, Journal of Personality and Social Psychology 1980, Vol 39, no. 6
(Un)Pleasantness
of the emotion
stimulus

26. J. A. Russell, A circumplex model of affect, Journal of Personality and Social Psychology 1980, Vol 39, no. 6

27. Emotion self-report
Valence (Un)Pleasantness of the
emotion stimulus
Arousal Level of activation of the
emotion stimulus
Dominance A person’s
perception of being in control of a
situation

28. Perceived productivity
Self-assessed
Values ranging from very low to
very high

29. Self-reported Emotions and
Productivity

30. Developer’s emotions

31. Developer’s emotions

32. Correlation Between
Emotions and Perceived
Productivity

33. Valence is positively correlated with
perceived productivity.
Interaction between valence and time
(morning vs. afternoon)
Emotions and self-assessed productivity

34. Valence is positively correlated with
perceived productivity
Stronger correlation in the afternoon.
Conversely, the correlation between
dominance and productivity is
stronger in the morning.
This could be due to fatigue, which is
known to impair emotion regulation.
Emotions and self-assessed productivity

35. Why are Developers (Un)Happy ?

36. “Did you experience
anything that might have
affected your emotions
during the last session?”

37. Positive: 18 Negative: 19
Triggers of emotions
350 answers
48

38. Emotion triggers

39. Emotion triggers

40. Positive emotions triggers
High perceived
productivity

41. Code quality
Positive emotions triggers

42. Effective
Collaboration
Positive emotions triggers

43. Positive emotions triggers
Breaks

44. Negative emotions triggers
Low perceived
productivity

45. Negative emotions triggers
Fatigue

46. Negative emotions triggers
Code
comprehension

47. Negative emotions triggers
Poor code and
tooling

48. Negative emotions triggers
Time pressure

49. A recipe for happiness?

50. A recipe for happiness?

52. Developers discuss their positive and negative
feelings about events and difficulties encountered
during retrospective meetings

53. “
“

54. Westerink et al. Deriving a Cortisol-Related Stress Indicator From Wearable Skin Conductance Measurements: Quantitative Model & Experimental Validation, Frontiers in Computer Science (2020)

55. Galvanic skin response (GSR)
• Electrical activity of the skin
• Changes due to the variation in body
sweating
• Electrical changes in the skin could be due
to variation in emotions

58. Can we recognize developers’ emotions at the workplace using lightweight
biometric sensors?

60. https://www.eecs.qmul.ac.uk/mmv
/datasets/deap/

61. https://www.eecs.qmul.ac.uk/mmv
/datasets/deap/

62. Sensor-based recognition of developers’ emotions

63. Minimal sensor setting for emotion recognition while
programming
Lab study

64. Machine Learning
Sensor-based Emotion Recognition
Empatica E4

65. Field study
Our classifiers outperform the baseline

66. Field study
The performance is
comparable to the one
in the lab
Lab study

68. Leave-one-subject-out

69. Sensor-based emotion recognition
Hold-out scores are higher than leave-one-subject out

70. Variability between participants
Also observed in the lab study (Girardi et al., ICSE 2020)

71. • Individual training/fine-tuning of emotion
classifiers
• Further validation with larger/more
diverse pool of participants from different
companies
• Self-disclosure of negative emotions
• Analysis of GSR peaks as proxy of stress
Open challenges

72. Open challenges
• Individual training/fine-tuning of emotion
classifiers
• Self-disclosure of negative emotions
• Analysis of GSR peaks as proxy of stress

73. Open challenges
• Individual training/fine-tuning of
emotion classifiers
• Self-disclosure of negative
emotions
• Analysis of GSR peaks as proxy of
stress

74. Open challenges
• Individual training/fine-tuning of emotion
classifiers
• Self-disclosure of negative emotions
• (Mis)alignment of different data sources
• Analysis of GSR peaks as proxy of stress

75. Emotion as a coherent response
among different components
(Pekrun)
Multiple emotion assessment methods might not align
at a particular moment in time
At the cognitive level, the emotion is triggered by
the assessment of a situation (i.e., worrying about
something threatening my goals).
At a physical level, emotions reflect in biometrics
changes (e.g, EDA changes due to sweating and
heart rate rising in presence of anxiety) and might
be also visible through facial expressions.
J. M. Harley, et al. “A multi-componential analysis of emotions during complex learning with an intelligent
multi-agent system,” Computers in Human Behavior, vol. 48, pp. 615–625, 2015.
R. Pekrun, Emotions as Drivers of Learning and Cognitive Development. New York, NY: Springer New
York, 2011, pp. 23–39.

76. • 23 participants
• Java development tasks
Girardi et al. (ICSE 2020)
Gold standard: facial expression
-100 100
Valence in
{Negative, Neutral, Positive}

78. F1 = .59
>>
F1 = .68
Self-report
as gold standard
Facial expression
as gold standard

79. Ethical and Privacy
concerns
Reduced
invasiveness

80. Ethical and Privacy
concerns
Reduced
invasiveness
• Focus on the individual:
providing individual
providing personalized
support
• Awareness of the possibility
of misclassification
• Focus on the team: sharing
on a voluntary basis

84. @NicoleNovielli