# Impact of AI-tooling on the Engineering Workspace

arXiv: https://arxiv.org/abs/2406.07683 · HTML: https://mlsanity.com/paper/2406.07683/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming](https://mlsanity.com/paper/2210.14306/) (2022)
- [On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot](https://mlsanity.com/paper/2302.00438/) (2023)
- [Practices and Challenges of Using GitHub Copilot: An Empirical Study](https://mlsanity.com/paper/2303.08733/) (2023)
- [Study of software developers' experience using the Github Copilot Tool in the software development process](https://mlsanity.com/paper/2301.04991/) (2023)

## Built on by (most cited first, 3 of 3)

- [AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development](https://mlsanity.com/paper/2601.13597/) (2026)
- [Do These Violent Delights Have Violent Ends? Measuring the Post-Merge Fate of Agentic Code](https://mlsanity.com/paper/2607.09902/) (2026)
- [A Few Pages of Markdown: Committed AI Configuration and Lower Quality Cost after Coding-Agent Adoption](https://mlsanity.com/paper/2608.25241/) (2026)

## Abstract (arXiv)

To understand the impacts of AI-driven coding tools on engineers' workflow and work environment, we utilize the Jellyfish platform to analyze indicators of change. Key indicators are derived from Allocations, Coding Fraction vs. PR Fraction, Lifecycle Phases, Cycle Time, Jira ticket size, PR pickup time, PR comments, PR comment count, interactions, and coding languages. Significant changes were observed in coding time fractions among Copilot users, with an average decrease of 3% with individual decreases as large as 15%. Ticket sizes decreased by an average of 16% across four companies, accompanied by an 8% decrease in cycle times, whereas the control group showed no change. Additionally, the PR process evolved with Copilot usage, featuring longer and more comprehensive comments, despite the weekly number of PRs reviewed remaining constant. Not all hypothesized changes were observed across all participating companies. However, some companies experienced a decrease in PR pickup times by up to 33%, indicating reduced workflow bottlenecks, and one company experienced a shift of up to 17% of effort from maintenance and support work towards product growth initiatives. This study is the first to utilize data from more than one company and goes beyond simple productivity and satisfaction measures, considering real-world engineering settings instead. By doing so, we highlight that some companies seem to benefit more than others from the use of Copilot and that changes can be subtle when investigating aggregates rather than specific aspects of engineering work and workflows - something that will be further investigated in the future.