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Are Happy Developers more Productive?

Are Happy Developers more Productive?. The Correlation of Affective States of Software Developers and their self-assessed Productivity. Daniel Graziotin, Xiaofeng Wang, Pekka Abrahamsson Free University of Bozen-Bolzano. PROFES 2013, 12-14 June, Paphos , Cyprus. Introduction

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Are Happy Developers more Productive?

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  1. Are Happy Developers more Productive? The Correlation of Affective States of Software Developers and their self-assessed Productivity Daniel Graziotin, Xiaofeng Wang, Pekka Abrahamsson Free University of Bozen-Bolzano PROFES 2013, 12-14 June, Paphos, Cyprus

  2. Introduction Motivation, Research Questions 1 Background Theory, Related Work, Constructs, Hypotheses 2 Research Methodology Research Design, Analysis Method 3 Results Descriptive Statistics, Hypotheses Testing 4 Discussion Implications, Limitations, Future Studies 5

  3. To improve Software Productivity and Quality, focus on people (Boehm, 1990)

  4. “People trump Process” (Cockburn & Highsmith, 2001)

  5. How to verify this claim?

  6. How to focus on people in SE Research? Picture Credits

  7. Little is known on the productivity of individual programmers (Scacchi, 1995) Picture Credits

  8. Human Factors, multidisciplinary Intellectual Activities Picture Credits

  9. Software Developmentis Cognitive(Khan et al., 2010) Picture Credits

  10. Affective StatesEmotions, Moods, Feelings Management and Psychology Influence on: • cognitive activities • working behaviors • Productivity (Ashkanaasy et al., 2002; Fisher et al., 2004; Ilies et al., 2002; Miner et al., 2010) Picture Credits

  11. Software Engineering Empirical studies should.. .. collect psychometrics(Feldtet al., 2008) .. focus on Affective States of Software Developers(Shaw, 2004 & Khan, 2010) Picture Credits

  12. Research Question How do the affective states related to a software development task in foci influence the self-assessed productivity of developers?

  13. Introduction Motivation, Research Questions 1 Background Theory, Related Work, Constructs, Hypotheses 2 Research Methodology Research Design, Analysis Method 3 Results Descriptive Statistics, Hypotheses Testing 4 Discussion Implications, Limitations, Future Studies 5

  14. Background Theory Emotions • states of mind raised by external stimuli, directed toward the stimulus by which they are raised (Plutchiket al., 1980) Moods • individual feels “good” or “bad”; “likes” or “dislikes” what is happening around (Parkinson et al., 1996) No agreement in literature Affective States: umbrella term Defining Affective States

  15. Background Theory Real-time positive Affective States are positively correlated with real-time performance of workers (Fisher et al., 2004) Affective States of Software Developers dramatically change during a period of 48 hours (Shaw, 2004) Positive Affective States have positive impact on software developers debugging abilities (Khan et al., 2010) Affective States and their Impact on Workers

  16. Background Theory Discrete Approach (Plutchik et al., 1980) • Basic set of unique Affective States • Example: interested, excited, guilty, upset Dimensional Approach (Russel, 1980) • Major dimensions • Valence • Arousal • Dominance Categorize Affective States

  17. Background Theory Questionnaires, surveys Self-assessment Manikin (SAM) (Bradley, 1994) • Assess Affective States triggered by stimulus • Pictorial (universal) • Likert-items Measure Affective States

  18. SAM - Valence

  19. SAM - Arousal

  20. SAM - Dominance

  21. Background Theory SAM items range: [1,5] Not stable across persons Stable within persons • Conversion to Z-scores • Dimensionless, range: [-3,+3] Measure Affective States

  22. Introduction Motivation, Research Questions 1 Background Theory, Related Work, Constructs, Hypotheses 2 Research Methodology Research Design, Analysis Method 3 Results Descriptive Statistics, Hypotheses Testing 4 Discussion Implications, Limitations, Future Studies 5

  23. Research Methodology Repeated measurements design Natural Settings Participants work on their software project for 90 minutes Affective States and Productivity self-assessed each 10 minutes Productivity Likert item [1,5] Research Design

  24. Research Methodology Research Design Development Pre-task Interview Post-task Interview Questionnaire Researcher

  25. Research Methodology Sam and Questionnaire on tablet device 4 “taps” to complete the survey • Limit context switch Pre- and post-task interviews In-field observations Data Collection

  26. Research Methodology Repeated measurements of individuals Data Dependencies • Participant • Time per participant Anova does not fit (Gueorguieva et al., 2004) Linear mixed-effects models Analysis Procedure

  27. Research Methodology Linear Mixed Effects Model • Fixed effects • Random effects Analysis Procedure Known Regressors I.i.d random error terms Parameters fixed value Random variables

  28. Introduction Motivation, Research Questions 1 Background Theory, Related Work, Constructs, Hypotheses 2 Research Methodology Research Design, Analysis Method 3 Results Descriptive Statistics, Hypotheses Testing 4 Discussion Implications, Limitations, Future Studies 5

  29. Results 8 Participants (mean age 23.75) • 4 Professional Software Developers • 4 Students 8 Projects • Work-related • Course-related Descriptive

  30. Results Descriptive

  31. Results Productivity and Valence

  32. Results Productivity and Arousal

  33. Results Productivity and Dominance

  34. Results First Observations For all participants • Strong variations of Productivity and Affective States • In 90 minutes of time

  35. Results Hypotheses Testing Linear Mixed Effects Model with R (lme4.lmer) • productivity ~ fixed + random • Fixed: • Random:

  36. Results Hypotheses Testing

  37. Results Scalar random effects for participants: [-0.48, 0.33]; Time Random effects estimated to 0. Hypotheses Testing

  38. Introduction Motivation, Research Questions 1 Background Theory, Related Work, Constructs, Hypotheses 2 Research Methodology Research Design, Analysis Method 3 Results Descriptive Statistics, Hypotheses Testing 4 Discussion Implications, Limitations, Future Studies 5

  39. Discussion Support for • positive correlation with valence, dominance and productivity No support for • positive correlation with arousal and productivity • interaction between affective states and time. The model • Estimates valence to 0.10, dominance to 0.48 (Z-scores) • Explains 25.62% of productivity deviance • 12.39% for valence and 11.71% for dominance. Implications

  40. Discussion Attractiveness perceived towards the development task (valence) Perception of possessing adequate skills (dominance) Almost the same explanation power The productivity was self-assessed by “deltas” of the previous input and the expectation of the task Implications

  41. Discussion This work • Provides basic theoretical building blocks on researching the human side of software construction. • Performs empirical validation of psychometrics and related measurement instruments in Software Engineering research. • Introduces rarely employed analysis methods Implications

  42. Discussion Limited Number of Participants, task duration • Background Skills balanced • All 72 measurements are valuable • Still typical number of participants and measurements (Vickers, 2003) Limitations

  43. Discussion Use of self-assessed productivity • Software metrics difficult to be employed • Productivity still open problem • Self-assessed productivity consistent to objective measurements of performance (Miner, 2010) Limitations

  44. Discussion Student employment • Next generation of software developers (Kitchenham et al., 2002) • Close to the actual population (Tichy, 2000) Individuals working alone • Control Purposes • Limit network of affective states Limitations

  45. Are Happy DevelopersMore Productive? • Towards “Yes, they are” • Definitive Answer • Multidisciplinary theories • Validated instruments • Open Mind Picture Credits

  46. Future Studies Same programming task Software teams New understanding of software development. Traditional software productivity metrics Mood induction techniques

  47. Software developers are unique human beings. • Perception of development life-cycle • Cognitive activities affect performance • New understanding of software development.

  48. Thank you for your attention Daniel Graziotin daniel.graziotin@unibz.it

  49. References Boehm, B.: Understanding and Controlling Software Costs. IEEE Transactions on Software Engineering 14(10), 1462–1477 (1990) Cockburn, A., Highsmith, J.: Agile software development, the people factor. IEEE Computer 34(11), 131–133 (2001) Scacchi, W.: Understanding Software Productivity. Advances in Software Engineering and Knowledge Engineering 4, 37–70 (1995) Khan, I.A., et al.: Do moods affect programmers’ debug performance? Cognition, Technology & Work 13(4), 245–258 (2010)

  50. References Ashkanasy, N.M., Daus, C.S.: Emotion in the workplace: The new challenge for managers. The Academy of Management Executive 16(1), 76–86 (2002) Fisher, C.D., Noble, C.: A Within-Person Examination of Correlates of Performance andEmotions While Working. Human Performance 17(2), 145–168 (2004) Ilies, R., Judge, T.: Understanding the dynamic relationships among personality, mood, and job satisfaction: A field experience sampling study. Organizational Behavior and Human Decision Processes 89(2), 1119–1139 (2002)

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