How to measure the real impact of AI coding assistants

Recent reports from Cognition reveal that Devin, an AI-powered software engineer, has tripled its productivity by using the latest GPT-o1 model. The adoption of AI assistants is becoming a crucial enabler in modern software engineering, with studies showing performance gains ranging from 5% to over 50%, depending on the nature of tasks, system architecture, and the implementation of best practices and standards. Even modest improvements can provide a competitive edge.

Ways to integrate AI assistants

1. Leveraging existing solutions. Tools like GitHub Copilot or Cursor are tied to specific large language models (LLMs).
2. Using flexible IDE plugins. Options like CodeGPT connect to multiple LLMs, allowing developers to choose the best model for the task.
3. Custom AI Assistants. Tailored solutions offer full customization and control but come with increased complexity and resource demands.

Metrics that matter

One of the key challenges in adopting AI assistants is measuring the actual performance boost. While developer surveys remain the primary tool, combining them with a data-driven approach provides deeper insights. Three important metrics to track:

1. Active Daily Users. The number of developers actively using the AI assistant.
2. Acceptance Rate. The percentage of AI-generated suggestions accepted by developers.
3. Code Retention Rate. The amount of AI-suggested code retained in the codebase over time (for example after 10 seconds, 1 minute, 10 minutes).

Code retention rate is the metric that tells you whether accepted suggestions actually stick. Acceptance rate can look healthy while most AI code gets edited away minutes later.

Tracking these metrics provides valuable insights into the effectiveness of AI assistants and their real impact on development processes.