The current software documentation process can be painful. Formal DevSecOps software documentation processes are inadequate, time consuming, and difficult to verify quantitatively. In any given Agile continuous integration/continuous deployment (CI/CD) software development lifecycle (SDLC) methodology, crafting and maintaining high-quality software documentation content can be a subjective, tedious, meticulous process requiring significant understanding and domain knowledge. What’s more, in modern Agile CI/CD or DevSecOps sprinting paradigms, human-in-the-loop (HITL) software documentation blockers detract from development success-gauging metrics. This situation inspires negative perceptions of current documentation processes and efforts to mitigate the blocker through substandard (or even non-existent) iterative documentation efforts. The README project is researching a machine learning (ML) application to generate descriptive content for automated software documentation. Specifically, the application will define, exemplify, and champion an approach to a generative software documentation process in the modern SDLC, transforming the art of software documentation into a science.
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README: A Learned Approach to Augmenting Software Documentation the online summary booklet
