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Automatic documentation generation tools, or auto docs, are widely used to visualize information about APIs. However, each auto doc tool comes with its own unique representation of API information. In this paper, I use an information visualization analysis of auto docs to generate potential design principles for improving their usability. Developers use auto docs as a reference by looking up relevant API primitives given partial information, or leads, about its name, type, or behavior. I discuss how auto docs can better support searching and scanning on these leads, e.g. by providing more information-dense visualizations of method signatures.
Effort estimation is an integral part of activities planning in Agile iterative development. An Agile team estimates the effort of a task based on the available information which is usually conveyed through documentation. However, as documentation ha
Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells. In this paper, we propose a new task of code documentation generation (CDG) for computational notebooks. In contrast to the previous CDG
Various software features such as classes, methods, requirements, and tests often have similar functionality. This can lead to emergence of duplicates in their descriptive documentation. Uncontrolled duplicates created via copy/paste hinder the proce
Web API specifications are machine-readable descriptions of APIs. These specifications, in combination with related tooling, simplify and support the consumption of APIs. However, despite the increased distribution of web APIs, specifications are rar
It is integral to test API functions of widely used deep learning (DL) libraries. The effectiveness of such testing requires DL specific input constraints of these API functions. Such constraints enable the generation of valid inputs, i.e., inputs th