Daily Questionnaire to Assess Self-Reported Well-Being during a Software Development Project

According to authors best knowledge, this workshop paper makes two novel extensions to software engineering research. First, we create and execute a daily questionnaire monitoring the work well-being of software developers through a period of eight months. Second, we utilize statistical methods developed for discovering psychological dynamics to analyze this data. Our questionnaire includes elements from job satisfaction surveys and one software development specific element. The data were collected every day for a period of 8 months in a single software development project producing 526 answers from eight developers. The preliminary analysis shows the strongest correlations between hurry and interruptions. Additionally, we constructed temporal and contemporaneous network models used for discovering psychological dynamics from the questionnaire responses. In the future, we will try to establish links between the survey responses and the measures collected by conducting software repository mining and sentiment analysis.

[1]  Denny Borsboom,et al.  Discovering Psychological Dynamics: The Gaussian Graphical Model in Cross-sectional and Time-series Data , 2016 .

[2]  Pekka Abrahamsson,et al.  Are Happy Developers more Productive? The Correlation of Affective States of Software Developers and their self-assessed Productivity , 2013, PROFES.

[3]  A. Bakker,et al.  When do job demands particularly predict burnout? The moderating role of job resources , 2007 .

[4]  Y Fujigaki,et al.  Time series investigation of job-events and depression in computer software engineers. , 1996, Industrial health.

[5]  Mariko Nishikitani,et al.  Influence of overtime work, sleep duration, and perceived job characteristics on the physical and mental status of software engineers. , 2005, Industrial health.

[6]  Maarit Laanti,et al.  Agile and Wellbeing -- Stress, Empowerment, and Performance in Scrum and Kanban Teams , 2013, 2013 46th Hawaii International Conference on System Sciences.

[7]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[8]  Sari Kujala,et al.  Finnish physicians’ stress related to information systems keeps increasing: a longitudinal three-wave survey study , 2017, BMC Medical Informatics and Decision Making.

[9]  Evangelia Demerouti,et al.  Job resources buffer the impact of job demands on burnout. , 2005, Journal of occupational health psychology.

[10]  Arthur E. Hoerl,et al.  Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.

[11]  Gloria Mark,et al.  The cost of interrupted work: more speed and stress , 2008, CHI.

[12]  Harri Lindholm,et al.  Short-Term Physiological Strain and Recovery among Employees Working with Agile and Lean Methods in Software and Embedded ICT Systems , 2017, Int. J. Hum. Comput. Interact..

[13]  Anneli Leppänen,et al.  Validity of a single-item measure of stress symptoms. , 2003, Scandinavian journal of work, environment & health.

[14]  T. A. Wright,et al.  Psychological well-being and job satisfaction as predictors of job performance. , 2000, Journal of occupational health psychology.

[15]  Joseph S. Valacich,et al.  The effects of task complexity and time availability limitations on human performance in database query tasks , 2005, Int. J. Hum. Comput. Stud..

[16]  M. Yuan,et al.  Model selection and estimation in the Gaussian graphical model , 2007 .

[17]  A. Bakker,et al.  The job demands-resources model : state of the art , 2007 .

[18]  Mika Mäntylä,et al.  More testers - The effect of crowd size and time restriction in software testing , 2013, Inf. Softw. Technol..