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Pre-AI Baseline: Developer IDE Satisfaction and Tool Autonomy in 2022

Nikola Balić

TL;DR

This study analyzes valid satisfaction data from 1,155 software developers collected in July 2022, immediately preceding the mainstream adoption of generative AI tools, establishing a verifiable baseline for longitudinal research into the productivity-satisfaction misalignment observed in the post-AI era.

Abstract

To quantify the impact of AI on software development, the community requires a robust pre-AI baseline. This study analyzes valid satisfaction data from 1,155 software developers collected in July 2022, immediately preceding the mainstream adoption of generative AI tools. We report a high-satisfaction ecosystem (Mean = 8.14 [95% CI 8.01-8.25]), dominated by Visual Studio Code (79% usage). Multivariable regression confirms that autonomy in tool choice is the strongest predictor of IDE satisfaction (beta = 0.51), significantly outweighing demographic or role-based factors. Conversely, cloud IDE adoption was negligible (4.3% regular usage), with 40.1% citing network dependency as the primary barrier, a constraint that remains relevant for modern cloud-reliant AI agents. Additionally, we identify an "experimenter" segment (29.9%) characterized by high tool churn but no significant satisfaction difference (t = 0.43, p = 0.67), and demonstrate significant variation in IDE retention rates (VS Code: 68.5%, traditional IDEs: 3.9-25%), suggesting underlying dissatisfaction despite high overall satisfaction. By providing a quantitative snapshot of developer sentiment and workflows on the eve of the AI revolution, this study establishes a verifiable baseline for longitudinal research into the productivity-satisfaction misalignment observed in the post-AI era.

Pre-AI Baseline: Developer IDE Satisfaction and Tool Autonomy in 2022

TL;DR

This study analyzes valid satisfaction data from 1,155 software developers collected in July 2022, immediately preceding the mainstream adoption of generative AI tools, establishing a verifiable baseline for longitudinal research into the productivity-satisfaction misalignment observed in the post-AI era.

Abstract

To quantify the impact of AI on software development, the community requires a robust pre-AI baseline. This study analyzes valid satisfaction data from 1,155 software developers collected in July 2022, immediately preceding the mainstream adoption of generative AI tools. We report a high-satisfaction ecosystem (Mean = 8.14 [95% CI 8.01-8.25]), dominated by Visual Studio Code (79% usage). Multivariable regression confirms that autonomy in tool choice is the strongest predictor of IDE satisfaction (beta = 0.51), significantly outweighing demographic or role-based factors. Conversely, cloud IDE adoption was negligible (4.3% regular usage), with 40.1% citing network dependency as the primary barrier, a constraint that remains relevant for modern cloud-reliant AI agents. Additionally, we identify an "experimenter" segment (29.9%) characterized by high tool churn but no significant satisfaction difference (t = 0.43, p = 0.67), and demonstrate significant variation in IDE retention rates (VS Code: 68.5%, traditional IDEs: 3.9-25%), suggesting underlying dissatisfaction despite high overall satisfaction. By providing a quantitative snapshot of developer sentiment and workflows on the eve of the AI revolution, this study establishes a verifiable baseline for longitudinal research into the productivity-satisfaction misalignment observed in the post-AI era.
Paper Structure (67 sections, 5 figures, 11 tables)

This paper contains 67 sections, 5 figures, 11 tables.

Table of Contents

  1. Classification Transparency and Sensitivity:
  2. Survey Development Process:
  3. Question Format Validation:
  4. Response Format Considerations:
  5. Ordinal Logistic Regression Sensitivity Analysis:
  6. Multiple Comparison Control and Effect Sizes:
  7. Modeling Details:
  8. Usage Classification:
  9. Net Promoter Score (NPS) and Recommendation Ratings:
  10. NPS Confidence Intervals:
  11. Tool autonomy
  12. Experimenter classification
  13. Sampling Limitations:
  14. Customer Value Analysis:
  15. Formal Interaction Testing:
  16. ...and 52 more sections

Figures (5)

  • Figure 1: Distribution of Net Promoter Score categories. The majority of developers (50.8%) were promoters, indicating strong overall satisfaction with their IDEs.
  • Figure 2: NPS scores by experience level. Satisfaction increases with years of coding experience; we emphasize mean satisfaction and effect sizes in the text, with NPS shown here for completeness.
  • Figure 3: Interaction between autonomy and experience on IDE satisfaction. The autonomy effect is consistent across experience levels, with no significant interaction term.
  • Figure 4: IDE satisfaction vs usage share (among respondents). VS Code shows both high usage and high satisfaction; JetBrains tools have lower usage with high satisfaction; traditional IDEs fall between.
  • Figure 5: Primary barriers to cloud IDE adoption. Network dependency is the most cited barrier (40.1%), exceeding all other concerns.