GitHub Copilot: A no-brainer with a huge return on investment!

Most software delivery teams are considering adopting AI?to help engineers accelerate their value delivery and increase effectiveness. GitHub Copilot, a pioneering AI-powered tool, serves as "your AI pair programmer," offering code autocompletion and much more. Recent updates have enhanced its capabilities, resulting in improved productivity and satisfaction among developers. But at $10 per user per month, how can a business justify this expense?

After evaluating the tools and talking with developers, I realized that Github Copilot is a game changer. Though the tool's newness makes it difficult to measure its value fully, the initial impressions seem extremely positive.?

Consider this: the average cost of a local contract software developer is $120 an hour. In light of this, the cost savings offered by GitHub Copilot are not just significant; they're game-changing. Even if the toolset saves developers just two hours a week, the return on investment is a staggering 100-fold, making the financial case for its adoption undeniable.?

In addition, the platform reduces errors and bugs because it can generate code directly from Copilot and validate the quality using the platform.

Understanding GitHub Copilot

GitHub Copilot uses OpenAI's?Codex to suggest code and entire functions in real-time from your editor.?Continuous improvements, like advanced contextual filtering and the Fill-In-the-Middle (FIM) paradigm, have made its suggestions even more relevant and useful. Developers report coding up to 55% faster and accepting 10% more suggestions due to these enhancements?(The GitHub Blog)?(The GitHub Blog).

Measuring the Impact

A robust methodology is essential to evaluate the impact of GitHub Copilot. Specific measurements include,

• Quantitative: Based on?measurable data.
• Holistic: Considering all benefits and potential impacts across the?SDLC.
• Balanced: Combining survey data with software delivery metrics.

A metrics scorecard reflecting the SPACE framework for developer productivity can capture the benefits and costs effectively.

Key Metrics to Track

1. Velocity and Throughput: These are measures of team output, such as throughput per engineer, sprint velocity, and work carried over by sprint. These metrics show how much work is completed over time.
2. Time to Value: Cycle time from development start to deployment and code cycle time from pull request to merge. These metrics highlight the speed of delivering software increments.
3. Quality: Metrics like escaped defects and build failure rates indicate the quality of the software being produced.
4. Dependability: Sprint target completion rates measure how reliably teams meet their goals.
5. Developer Satisfaction: Employee Net Promoter Scores (eNPS) reflect developers' satisfaction and well-being, which are?crucial for long-term productivity?(The GitHub Blog)?(The GitHub Blog).

Empirical Data and Business Case

Studies conducted by Gartner and Microsoft show that developers using GitHub Copilot complete tasks significantly faster—up to 55% faster in controlled experiments. This productivity gain is reflected in higher acceptance rates of code suggestions and overall job satisfaction. For instance, Duolingo reported a 25% increase in developer velocity using GitHub Copilot?(The GitHub Blog).

Copilot for Github is not just a tool for faster coding; it enhances the entire developer experience, making it a valuable investment for modern software delivery teams.