As generative AI (GenAI) tools like GitHub Copilot become increasingly integrated into software development workflows, monitoring and measuring their impact is essential. These tools enhance developer experience (DevEx) and improve developers’ quality of life by automating repetitive tasks, suggesting code snippets and boosting overall productivity. However, organizations must measure productivity gains, code acceptance rates and other key metrics to understand the actual value of GenAI development tools. 

The Role of Monitoring and Measurement 

Monitoring and measurement are critical in understanding the effectiveness of GenAI tools. More is needed to gauge adoption rates and developer engagement alone. Organizations must also understand how these tools impact the overall development process, from DevEx and usage metrics to downstream metrics like those outlined in the DORA framework.   

While it is important to see that developers are using tools like GitHub Copilot, what is even more important is having the complete picture by measuring how their code affects throughput, velocity and other key performance indicators (KPIs). For example, GitHub conducted a study revealing that developers who don’t use Copilot take twice as long to complete tasks as those who do. This time-saving potential is a significant selling point for GitHub, but organizations must ask themselves: How are we measuring these time savings? How do we assign value to the time saved? How does this relate to DevEx overall? Let’s take a closer look into how to appropriately measure the impact that GenAI tools have on an organization.  

Measuring the Impact of GenAI Tools 

To fully understand the impact of GenAI tools, organizations need to consider a range of factors: 

• Time Savings: How much time are developers saving by using tools like GitHub Copilot? This is about completing tasks faster and freeing up time for more strategic activities, such as addressing technical debt or innovating new features. 

• Developer Experience: A positive DevEx can lead to better retention rates, quicker ticket resolution and higher job satisfaction. It is essential to measure whether using GenAI tools improves developers’ overall work-life balance, allowing them to focus on more creative and fulfilling tasks. 

• Downstream Metrics: Tools like GitHub Copilot should be evaluated for their immediate impact and downstream metrics such as deployment frequency, mean time to recovery (MTTR) and other DORA metrics. If developers are more engaged with these tools, does this lead to more frequent deployments or faster recovery times? These metrics provide a fuller picture of the tool’s impact on the entire software delivery lifecycle. 

Different organizations will have different goals when it comes to adopting GenAI tools. Some may prioritize freeing up developer time to focus on strategic initiatives, while others might allow developers to work on passion projects once the tedious tasks are completed. In both cases, time savings are crucial — but what developers do with that saved time can vary widely. Representative vendors offering GenAI metrics and dashboards include Jellyfish, LinnearB, Faros.AI and Opsera.  

The Need for Comprehensive Metrics 

While many organizations may utilize various dashboards and metrics to measure success and performance, having a comprehensive view of the entire software delivery lifecycle (SDLC) is critical. A single team’s metric might look promising, but that does not necessarily mean the whole organization benefits. To get the full picture, it is essential to integrate data from all tools used across the SDLC, including Jira, security tools and source code management (SCM) systems. 

Organizations can better understand how GenAI tools like GitHub Copilot impact individual teams and the entire business by having a unified perspective on performance across the board. This comprehensive approach allows for more informed decision-making and a clearer understanding of these tools’ return on investment (ROI). 

While GenAI tools offer significant potential to improve productivity and DevEx, their value can only be understood through rigorous monitoring and measurement. By taking a holistic approach that includes both upstream and downstream metrics, organizations can ensure they are fully leveraging these tools to drive success.