Microservice Documentation via Call-Context Graphs
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Solution Overview
Problem
Existing documentation solutions for microservice-based applications, such as DocumentX, Ddoc, and Doxygen, are inadequate as they are tailored for monolithic applications and fail to scale up to complex microservice applications, lacking the ability to generate user-centric workflow documentation that reflects feature integrations and actual user interactions effectively.
Innovation Solution
A system that generates documentation for microservice-based applications by accessing usage information, backlog tracker data, and code repositories to create a call-context graph, cross-matching usage data with the graph to produce an aggregated output, which is then used by a natural language generator to produce user-focused documentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing documentation solutions (DocumentX, Ddoc, Doxygen) are used for microservice-based applications, then they can provide basic documentation generation, but they fail to generate user-centric workflow documentation that reflects feature integrations and actual user interactions
Solution Approach 1:
The system segments the documentation generation process into distinct components: a usage information collector that gathers actual user interaction data, a call-context graph generator that maps code relationships, and a natural language generator that produces documentation. This segmentation allows each component to specialize in specific tasks, enabling user-centric workflow documentation generation while managing overall system complexity through modular architecture.
Solution Approach 2:
The documentation generation system is designed to be universal by integrating multiple functions: it collects usage information from various sources, generates call-context graphs from code repositories, and produces different types of documentation (user flows, feature integrations, actual user interactions) through a single unified platform. This multi-functionality enables the system to adapt to diverse microservice architecture documentation needs without requiring separate specialized tools.
2Manufacturing precision
If manual documentation creation is performed for microservice-based applications, then comprehensive and accurate documentation can be achieved, but it is time-consuming and difficult to maintain
Solution Approach 1:
The documentation generation system operates autonomously by automatically collecting usage information from production environments, generating call-context graphs from code repositories, and producing updated documentation without manual intervention. This self-service capability ensures documentation accuracy reflectss actual system behavior while dramatically reducing the time investment required compared to manual documentation processes.
Solution Approach 2:
The system implements continuous feedback loops where usage information collected from actual user interactions feeds back into the documentation generation process. This feedback mechanism ensures documentation remains accurate and synchronized with the actual behavior of microservices, automatically updating user flows and feature integrations based on real-world usage patterns without requiring manual review or correction.
3Productivity
If traditional documentation approaches are used for monolithic applications, then documentation can be generated for the application structure, but they fail to scale up to complex microservice applications with multiple services and integrations
Solution Approach 1:
The system introduces a call-context graph as an intermediary representation that bridges the gap between code repositories and final documentation. This intermediate structure captures the complex relationships between microservices, their dependencies, and integration patterns in a manageable format. The call-context graph serves as a mediator that translates raw code data into structured information that can be easily processed into comprehensive documentation, handling microservice complexity without overwhelming the documentation generation process.
4Adaptability or versatility
If usage information and code analysis are integrated to generate documentation, then user-centric workflow documentation can be produced, but more data processing and cross-matching operations are required
Solution Approach 1:
The system adds a temporal dimension to documentation generation by incorporating usage information collected from production environments. This temporal aspect transforms static code analysis into dynamic, behavior-based documentation. By cross-matching usage patterns with call-context graphs, the system creates user-centric workflows that reflect actual system behavior over time, enabling versatile user-centric documentation while managing data processing complexity through structured temporal analysis.
Data Source
AI summary
A system of one or more computers that are configured to generate documentation for microservice-based applications is disclosed. In at least one embodiment, usage information relating to actual usage by one or more users of a plurality of microservices, features extracted from user story information in a backlog tracker, and a call-context graph including information identifying calling functions and called functions in the plurality of microservices are used to generate data that is provided to a natural language generator, which uses the information to automatically generate documentation for the microservice based application.