Frontend: Code suggestions acceptance by language

Problem

Engineering leadership (Director/VP/CTO) need to demonstrate the ROI from the investments in AI features. Since AI features are enabled at the user level, Team leads want to understand which languages are being used for Code Suggestions

Older problem statement

Engineering leadership (Director/VP/CTO) need to demonstrate the ROI from the investments in AI features. Since AI features are enabled at the user level, Team leads want to understand which users are leveraging AI features and whether their performance has changed over time as a result of the AI usage.

1. To compare the performance of teams that are using AI against teams that are not using AI.
2. To track the progress of AI adoption for evaluating the potential of AI usage.

workflowproblem validation :

• Related to 1 and 2 Customer feedback, 3 and this example.

• Competitive example

Proposal

• 🖼️ Code suggestions acceptance rate by language

UI Specification:

1. Title: Code suggestions acceptance rate by language
2. Subtitle: Last 30 days
3. X-Axis: Acceptance rate (%)
4. Y-Axis: Programming Languages
5. Time Range: Default is 30 days but later customization will be needed.
6. Tooltip Support: Hovering over a bar will show acceptance rate as a %, plus the values that make up the %, Suggestions, Suggestions accepted.
7. Sorting: Bars sorted by language with the highest acceptance rate.

backend we already capture the language metadata and store it in ClickHouse when a code suggestion event is triggered (?)

Design source

❖ Figma project →

Open questions:

• Can we compare the number of LOC generated by AI vs the number of LOC generated by humans. Both Line of Code and Line of Comments ratio are interesting.