How To Design a POC To Evaluate AI Assistants

1. Identify a set of representative tasks or workloads. This could include a mix of new feature development, bug fixes, pull requests and infrastructure changes.
2. Recruit a team of developers with varying levels of experience and expertise. This will help to ensure that the results of the pilot are generalizable.
3. Randomly assign developers to one of the three AI-powered coding assistants.
4. Provide all developers with the same training and resources. This will help to minimize bias in the results.
5. Define Objectives and Key results across the end-to-end software development lifecycle
6. Track the following metrics for each developer:

Time to complete each feature or bug        

Number of bugs introduced        

Quality of code produced        

Number of Security vulnerabilities        

The specific details of the plan would need to be tailored to the specific needs of the organization. Here are some additional considerations:

• Use a variety of metrics to measure productivity. In addition to the metrics listed above, other relevant metrics could include:

• Percentage of code generated by the AI assistant
• Number of lines of code written per day
• Number of features shipped per sprint/iteration
• Acceptance rate by developer and programming language
• Customer satisfaction with the software product (NPS Surveys)
• Track DORA and SPACE metrics - both subjective and analytics across sprints or iterations.

• Use a control group. In addition to the three groups of developers using the AI-powered coding assistants, it would be helpful to have a control group of developers who are not using any AI-powered tools. This will help to establish a baseline for comparison.
• Use a statistical analysis tool to evaluate the results. This will help to determine whether any differences in productivity between the groups are statistically significant.

Sources:

1. GitHub Copilot documentation: https://github.com/features/copilot
2. AWS CodeWhisperer documentation: AI Code Generator – Amazon CodeWhisperer
3. https://cloud.google.com/blog/products/ai-machine-learning/duet-ai-in-google-cloud-preview
4. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/yes-you-can-measure-software-developer-productivity