Revolutionizing Technical Documentation with AI

Hello, I'm Vitaliy Shchur, let's talk about how AI is revolutionizing technical documentation, creation, aimement, and maintenance. I'm an experienced quality assurance engineer with over ten years of experience in testing web applications, mobile apps, desktop platforms. I contributed to the growth of a startup from 25 to 250 employees, which lead into its sale for half a billion dollars.

I have worked at companies like Checkpoint Software and Perimeter81 and others, focusing on testing, creating test cases, analyzing specifications, and cross-platform testing by extensive backgrounds, PANs, product data, and outsourcing companies in cybersecurity, fintech, and education.

Every IT engineer with experience in software development or testing knows that it is crucial to constantly optimize processes. Precise, well-structured technical documentation is a key driver of a successful project. Why is technical documentation so important? It plays a crucial role in software development. It serves as a single source of truth, ensuring that teams align in project requirements, test strategies, and implementation details. Poor documentation leads to miscommunications, the teams may misinterpret requirements, leading to costly errors. To reduce efficiency, developers and testers spend more time searching for information. To inconsistencies, different teams may follow different standards, leading to fragmented documentation.

With the rise of AI-powered tools, we now have a way to automate documentation processes, ensuring consistency, clarity, and efficiency, and make it creation and maintenance more easy. AI is revolutionizing technical documentation by the following. It is automating documentation generation. AI can create epics, user stories, and test cases based on the project descriptions. It ensures documentation consistency. AI applies a standardized glossary and terminology across all documents. It is enhancing documentation accuracy. AI can detect outdated content and suggest updates. It is improving readability and accessibility. AI can summarize complex information for different audiences and make it shorter. Tools such as ChargePT, Atlassian Intelligence, and GitHub Copilot are making documentation faster, more accurate, and easier to maintain.

How it can help us to gather technical requirements? Digital assistants analyze requirement documents, identify discrepancies in the text, inconsistencies in numbers, units of measurements, and sums, and suggest possible solutions. AI helpers for Zoom and other conference calls can recognize speech and generate follow-ups summarizing discussions. Another useful tool for requirements creation is Miro Assist. It helps teams in the early stages of product development when stakeholders are brainstorming ideas. This AI-powered tool enhances meetings by filling in gaps and providing structure. Key features of Miro Assist include creating notes that summarize discussions, covering text to images, converting text to images, mapping the user stories with user personas. It can create and sequence diagrams to review main ideas. Also like other copilots, it can generate code blocks using natural language.

Okay. Automating epics and user stories creation from the SDD. One of the time-consuming tasks in agile development is writing epics and user stories from software development documentation. AI can analyze SDDs and extract key functionalities. It can automatically generate well-structured epics and stories. It can help us to ensure that text is aligned with project requirements. And it is vastly reduced manual effort enabling teams to focus on implementation.

For example, before AI, manual process required a detailed review of the specification and spending a lot of time for it. And with AI, the system can scan documents and auto-generate structured epics and user stories, which need only a little effort to make them like perfect for the use. AI can help us keeping documentation up to date and up to date in content management systems. Outdated documentation can lead to confusion and inefficiencies. AI help us by detecting obsolete information. AI compares documentation with recent code changes. It suggests updates automatically. AI provides draft updates based on project evolution. It can help us apply a common glossary. AI ensures terminology is standardized across documents and unified, so that same entities do not get called by different names, which is very confusing, especially for the new employees. This approach eliminates inconsistencies and ensures that teams always have the latest information.

AI for generating test cases. Creating test cases manually is a tedious task. AI simplifies this process by understanding feature descriptions, AI reads feature documentation and generates test cases. Extracting test scenarios from SDD ensures full test coverage based on system requirements by generating structured test cases. AI formats test cases in a structured reusable format. This automation ensures comprehensive test coverage and reduces the risk of missing critical test scenarios. AI can help us with all-pairs testing creations. AI pairs all-pairs testing is an efficient way to test combinations of parameters while minimizing redundant test cases. AI can identify k-parameter combinations, generate optimizer test cases that cover all-pairs, reduce the overall number of test cases while maintaining maximum test coverage. This method significantly improves testing efficiency, particularly in complex systems with numerous configurations and a big number of parameters. AI for documentation editing and refinement. AI-powered tools can improve documentation by enhancing readability, AI simplifies complex technical language, auto-formatting AI-structured documents for consistency, detecting ambiguities, AI highlights unclear or incomplete sections.

These features ensure that documentation is well-structured, clear and easy to navigate. GitHub Copilot and Microsoft Copilot for Documentations. These AI tools can help developers to generate API documentation from code comments, automatically document features based on function descriptions, and provide suggestions for missing documentation. By leveraging AI, teams can ensure the documentation stays in sync with code changes.

AI for code commenting. Developers often neglect code comments. AI-powered copilots can automatically generate meaningful comments. They can explain complex logic in a structured way. They can suggest improvements for existing comments. This ensures better code maintainability and understanding for the future developers. Examples of such tools is GitHub Copilot, Microsoft Copilot, and Mintlify.

AI can help with drafting code. AI copilots can assist in coding by generating boilerplate code based on developer prompts, providing function templates with relevant comments, suggesting improvements to existing code. This speeds up development while maintaining clarity and structure. Like around 80% of the code generated by AI helpers are relevant and high quality, so it can speed up the development process and writing the code like in three or even five times for some projects and some developers.

AI for unit test creation. AI copilots, e.g. GitHub Copilot, Microsoft Copilot, assist in unit test creation by generating test code based on developer prompts. This accelerates unit test creation four or five times than traditional methods and significantly increases coverage and improves code quality and saves us from many bugs. How AI can help improving code quality. One of the best known tools for improving code quality is SonarQube. It performs in-depth code analyzes and generates reliability reports. Among its capabilities, we can find identifying and correcting code duplication and potential errors, integrating with corporate environments, support 19 popular programming languages, from Python and PHP to Kotlin and Swift. SonarQube ensures code quality and security. Its enterprise level features include a user-friendly graphical interface and seamless integration with corporate tools like ServiceNow. And using end user documentation.

Surprisingly, AI can eliminate the need for users to read documentation, chat bots, and virtual assistants. Assistants can provide instant personalized support by mimicking human interaction, understanding natural language and learning from every conversation. This helps us and chat bots result in highly efficient and deeply personalized user support. Like 80% of user problems could be solved with AI chat bots.

AI is great for creating website content. AI can cost-effectively automate content creation, making it essential for filling websites with text. Large language models, such as ChatGPT, CopyAI, and NeuronWriter can generate detailed, high-quality content about products, services, processes, and everything you need.

Now, let's move to the most interesting part. The challenges of AI-powered documentation. Despite its advantages, AI-driven documentation presents some challenges. Manual review is absolutely required. AI-generated content must be checked for accuracy by the specialist, by the professional with knowledge in the area. Context limitations. AI may miss subtle, project-specific details. So sometimes you need to add these inter-prompts to get correct results. Also, there are security concerns. AI tools must be integrated securely to protect sense of information. For example, if you are a competitor with Microsoft, you can't use Microsoft AI helpers because you don't know will they use the information that you type into these helpers or not. And initial productivity drop. Teams using AI tools may experience a short-term learning curve before AI can provide precise software development recommendations. But after that, usually the productivity only rises.

Best practices for AI-driven documentation content. To maximize AI benefits, we need to combine AI with human expertise. We need to ensure AI-generated content is reviewed. And we need to train AI models with relevant documentation styles. The future of AI in documentation. As AI evolves, we can expect that it will have improved accuracy in AI-generated documentation, more intuitive integration with development workflows, and increased automation in keeping documentation up-to-date. In conclusion, we can say the following. AI is transforming documentation, making it more automated, more consistent, more efficient. By leveraging AI-powered tools, teams can focus on development while ensuring high-quality documentations. We'd be glad to answer all your questions. Thank you for your time.