How to Automate Software Documentation with DevOps: Tools & Best Practices

Introduction

Formal documentation such as source code docs, system requirements, design docs, and user manuals is often ignored by development teams, but DevOps processes can alleviate this problem. Documentation types include code, requirements, design, system, and user documentation.

When documentation tools and processes are not aligned with development tools like version control, issue trackers, wikis, and source code, they can become obstacles and slow down development.

The article identifies three main challenges in documentation: workflow, code comments, and system documentation, and explains how DevOps‑based documentation can provide a common, trustworthy information source for all stakeholders.

Problem Workflow

Creating and maintaining user and system documentation often relies on bulky binary files (e.g., *.docx) and extensive email or shared‑folder exchanges, leading to inconsistencies across platforms and teams.

Storing binary files in version control is possible but managing multiple versions remains difficult, and automating documentation within the software development lifecycle introduces additional challenges.

As projects progress, documentation updates become less frequent or are abandoned entirely.

Teams must balance richness and simplicity in documentation.

Documentation and Code "No Separation"

Distinguishing objective data from subjective, organized material is crucial. Information should be stored where developers interact with it—ideally alongside the code it describes.

If documentation is not co‑located with code, any change to functionality requires developers to locate and update separate documents, slowing progress.

Generated documentation should be immutable once published, serving as a reference; publishing it as a web page ensures the latest version is always displayed.

Source Code

Commenting code has long been a best practice. Over the past decade, many tools have emerged to extract documentation from source code comments, allowing developers to understand code more easily.

Some tools also let programmers embed readable tests in documentation, causing builds to fail if code changes are not reflected in the docs.

Continuous integration provides rapid feedback to enforce proper documentation policies.

Examples of tools that generate readable documentation from source code comments include:

Ruby: RDoc

Python: Sphinx

Java: Javadoc

Ruby, Java, .NET, Flex: Cucumber

C++ and others: Doxygen

Managers often request exhaustive line‑by‑line comments, which can overwhelm developers and hinder timely delivery of business value.

In DevOps, everything should be automated, finding a balance between understandability and simplicity.

Recommendation: Adopt a "new objects should be documented" mindset to achieve "automatic documentation" and help developers add useful comments.

System, Design, and User Documentation

Tools for system, design, and user documentation are less abundant than those for code comments, often requiring organizations to build custom processes.

Red Hat’s senior technical writer advocates using continuous integration and unit‑tested documentation, employing tools like Hemingway or After the Deadline for grammar checks.

Using unit‑tested documentation ensures standardized docs across departments.

At DevOpsDays NYC 2015, Etsy described a process that dynamically records data‑center network infrastructure using Chef to update Nagios, automating documentation updates as engineers work.

Etsy’s approach automates the documentation update process, ensuring documentation reflects the current system state.

Our suggestion is to treat documentation as source code, version it, and enable individuals to merge small data sources with various documentation materials automatically.

Effective handling of controllable data reduces the negative impact of context switching.

Transitioning to a DevOps‑driven documentation workflow requires a mindset shift, selecting the right tools to generate documentation, and automating information generation to improve documentation quality and usability.