DORA began researching code documentation‘s impact on the software development lifecycle in 2021. Since then, their findings have consistently shown that high-quality documentation significantly impacts organizational performance.
Here’s a breakdown of the key documentation takeaways from the 2024 report:
The role of documentation in developer productivity
Internal documentation plays a pivotal role in developer productivity and team efficiency. When documentation is reliable and high-quality, developers spend less time searching for information and more time on their core responsibilities: building and maintaining software.
This year’s report quantifies this impact and highlights the growing importance of strong documentation practices in modern development.
High quality documentation enhances:
- Team efficiency: Developers can quickly find answers, reducing interruptions and bottlenecks
- Onboarding speed: New hires ramp up faster with clear, accessible documentation
- Flow and satisfaction: Reliable documentation reduces cognitive load, enabling developers to maintain flow and increase job satisfaction
AI’s role in transforming documentation
According to the 2024 DORA report, “AI is no longer ‘on the horizon’, but has fully arrived and is, quite likely, here to stay.” That shouldn’t be surprising to any of us at this point.
The impact of AI on the SDLC has resulted in a paradigm shift in development, and generating documentation and code explanations are some of the main tasks that developers rely on AI for.
AI adoption is transforming how teams approach documentation, with a 25% increase in AI use leading to a 7.5% improvement in documentation quality. However, this comes at a cost—delivery throughput and stability decrease by 1.5% and 7.2%, respectively.
As AI generates more code, an unintended consequence emerges: development processes can slow down as the sheer volume of code becomes harder to understand. Developers still need to understand the rationale behind code decisions and their alignment with business objectives, making clarity and maintainability crucial in AI-driven development.
Here’s how Swimm aligns with the 2024 DORA findings
DORA consistently links high-quality documentation to developer productivity, collaboration, and organizational performance.
Automating the creation of effective documentation
A 25% increase in AI adoption is associated with a 7.5% increase in documentation quality. At Swimm we leverage deep static code analysis and AI to take this even further:
- Generate context-aware documentation for entire repositories in just a few clicks
- Keeping documentation up to date as code evolves, ensuring reliability over time
- Integrating seamlessly into the development workflow,
Closing the gap between code and the context behind it
The DORA Report highlights a key challenge with AI-generated outputs: while the volume of code and documentation increases, developers still need to understand the why behind the code and how it connects to business objectives.
The Swimm knowledge layer bridges this gap:
- Documentation serves as a crucial context layer for LLMs
- Documentation is embedded in the codebase, with docs saved as MD files and discoverable alongside code in the IDE
Building (and maintaining) a culture of documentation
Although the Agile manifesto advocates for “working software over comprehensive documentation”, year after year, DORA continues to find that quality documentation is a key component of working software. Teams that incorporate documentation into their workflows, as a core part of the SDLC, benefit immensely.
Swimm helps promote a culture of documentation:
- Automatically keeping documentation up to date, even as code changes
- Integrating documentation into the SDLC, enhancing discoverability without having to switch contexts
- Generating documentation for entire repositories, making the creation of new documentation seamless
What you should take from the report
Once again the data is clear: strong documentation practices are essential for improving team performance and efficiency. Here’s how engineering leaders can act on these insights:
- Prioritize documentation practices: Make documentation a core part of your development process. Treat it as an ongoing responsibility, not a one-time task. Encourage developers to update and maintain documentation alongside code changes to ensure it stays current and relevant.
- Invest scalable AI solutions for documentation: AI-powered platforms reduce the manual burden on developers and ensure documentation is accurate and aligned with the latest codebase. This enables teams to focus on innovation rather than spending time managing outdated documentation.
- Measure the impact of documentation: Look at onboarding times, team productivity, and collaboration effectiveness. Use these insights to identify further opportunities for refinement and ensure documentation initiatives drive measurable outcomes.
Wrapping up
Once again, the annual DORA report reinforces that critical role of code documentation in modern software development. High-quality documentation not only boosts team productivity but also fosters collaboration and reduces burnout, and this report provides teams with actionable benchmarks to elevate their documentation practices.
