Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Identifying Factors Contributing to Bad Days for Software Developers: A Mixed Methods Study

Ike Obi, Jenna Butler, Sankeerti Haniyur, Brian Hassan, Margaret-Anne Storey, Brendan Murphy

TL;DR

This research employed a mixed-method approach, including interviews, surveys, diary studies, and analysis of developer telemetry data to uncover and triangulate common factors that cause “bad days" for developers.

Abstract

Software development is a dynamic activity that requires engineers to work effectively with tools, processes, and collaborative teams. As a result, the presence of friction can significantly hinder productivity, increase frustration, and contribute to low morale among developers. By contrast, higher satisfaction levels are positively correlated with higher levels of perceived productivity. Hence, understanding the factors that cause bad experiences for developers is critical for fostering a positive and productive engineering environment. In this research, we employed a mixed-method approach, including interviews, surveys, diary studies, and analysis of developer telemetry data to uncover and triangulate common factors that cause "bad days" for developers. The interviews involved 22 developers across different levels and roles. The survey captured the perception of 214 developers about factors that cause them to have "bad days," their frequency, and their impact on job satisfaction. The daily diary study engaged 79 developers for 30 days to document factors that caused "bad days" in the moment. We examined the telemetry signals of 131 consenting participants to validate the impact of bad developer experience using system data. Findings from our research revealed factors that cause "bad days" for developers and significantly impact their work and well-being. We discuss the implications of these findings and suggest future work.

Identifying Factors Contributing to Bad Days for Software Developers: A Mixed Methods Study

TL;DR

This research employed a mixed-method approach, including interviews, surveys, diary studies, and analysis of developer telemetry data to uncover and triangulate common factors that cause “bad days" for developers.

Abstract

Software development is a dynamic activity that requires engineers to work effectively with tools, processes, and collaborative teams. As a result, the presence of friction can significantly hinder productivity, increase frustration, and contribute to low morale among developers. By contrast, higher satisfaction levels are positively correlated with higher levels of perceived productivity. Hence, understanding the factors that cause bad experiences for developers is critical for fostering a positive and productive engineering environment. In this research, we employed a mixed-method approach, including interviews, surveys, diary studies, and analysis of developer telemetry data to uncover and triangulate common factors that cause "bad days" for developers. The interviews involved 22 developers across different levels and roles. The survey captured the perception of 214 developers about factors that cause them to have "bad days," their frequency, and their impact on job satisfaction. The daily diary study engaged 79 developers for 30 days to document factors that caused "bad days" in the moment. We examined the telemetry signals of 131 consenting participants to validate the impact of bad developer experience using system data. Findings from our research revealed factors that cause "bad days" for developers and significantly impact their work and well-being. We discuss the implications of these findings and suggest future work.

Paper Structure

This paper contains 37 sections, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. The Role of Emotions in Developer Productivity
  4. Measuring and Understanding Developer Productivity
  5. Methods
  6. Data Collection
  7. Interviews
  8. Survey
  9. Diary Study
  10. Telemetry Study
  11. Data Analysis
  12. Interview Data
  13. Survey Data
  14. Diary Data Analysis
  15. Telemetry Analysis
  16. ...and 22 more sections

Figures (6)

  • Figure 1: Our research process involved a mixed-method approach that allowed us to foreground and triangulate insights from different approaches
  • Figure 2: Number of participants in each of our mixed-method approaches
  • Figure 3: Demographic Profile of Survey Participants
  • Figure 4: Manual coding of the open-ended question "What Does a "Bad Day" Mean to You?" revealed the most frequent responses.
  • Figure 5: Self-Reported Frequency of Bad Days
  • ...and 1 more figures