Modern software documentation processes in development, security, and operations (DevSecOps) software development lifecycles (SDLCs) are inadequate, time consuming, and difficult to quantify quantitatively. Anecdotally, any software documentation process can be painful [Shorter 2020]. For any given continuous integration/continuous deployment (CI/CD) SDLC methodology, crafting and maintaining high-quality software documentation content can be a subjective, tedious, meticulous process requiring significant understanding and domain knowledge. Additionally, 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 research initiative is a strategic step forward towards a descriptive content generative process in modern DoD DevSecOps SDLCs. The README proof of concept (POC) is not a templating engine. Rather, the primary differentiator between the README POC approach and emerging approaches in Development, Documentation, and Operations (DevDocOps) is that software documentation content is directly inferred from the underlying source code itself, backed by the SDLC cadence via DevSecOps policy. The README approach relies on leveraging a machine learning (ML) modular architecture for learning the nuanced associations between Python3.8 source code and corresponding software engineering (SWE) descriptive lexicon language, in an unsupervised manner, from thousands of open source publicly available repositories’ commit transaction histories. The README POC release establishes a viable cross-domain forward inference POC model learned from software repositories, and a minimum viable product (MVP) DevSecOps service prototype of the model as an exemplar.

The README ML cross-domain translation architecture is defined as a latent translation bridging model nested between two pretrained models over orthogonal data modalities [Tian 2019]. The README project refers to the nesting-based approach of pretrained models for cross-domain latent translation as the “Matryoshka Technique,” facilitating domain modularity with a deeper network-forward through pretrained nested model reuse. The Matryoshka Technique provides a modular experimental harness for training and validation (T&V) of multiple pretrained models, under varying pretrained configuration hyper-parameterizations, for learning a nested, shared, latent space modeling structure between them.