Documentation of Machine Learning Software
This addresses documentation challenges for users with diverse backgrounds in machine learning, but it is incremental as it builds on existing techniques.
The paper tackles the problem of machine learning software documentation being unsuitable for non-expert users by proposing to analyze Stack Overflow Q/As to understand documentation issues and triggers, with the goal of automating documentation generation and adaptation for varying expertise levels.
Machine Learning software documentation is different from most of the documentations that were studied in software engineering research. Often, the users of these documentations are not software experts. The increasing interest in using data science and in particular, machine learning in different fields attracted scientists and engineers with various levels of knowledge about programming and software engineering. Our ultimate goal is automated generation and adaptation of machine learning software documents for users with different levels of expertise. We are interested in understanding the nature and triggers of the problems and the impact of the users' levels of expertise in the process of documentation evolution. We will investigate the Stack Overflow Q/As and classify the documentation related Q/As within the machine learning domain to understand the types and triggers of the problems as well as the potential change requests to the documentation. We intend to use the results for building on top of the state of the art techniques for automatic documentation generation and extending on the adoption, summarization, and explanation of software functionalities.