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README: A Learned Approach to Augmenting Software Documentation

November 2021 Presentation
Daniel DeCapria

The README project is researching a machine learning (ML) application to generate descriptive content for automated software documentation.

Publisher:

Software Engineering Institute

Abstract

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.