Automatic Identification and Extraction of Self-Claimed Assumptions on GitHub

In software development, due to the lack of knowledge or information, time pressure, complex context, and many other factors, various uncertainties emerge during the development process, leading to assumptions scattered in projects. Being unaware of certain assumptions can result in critical problems (e.g., system vulnerability and failures). The prerequisite of analyzing and understanding assumptions in software development is to identify and extract those assumptions with acceptable effort. In this paper, we proposed a tool (i.e., SCAMiner) to automatically identify and extract self-claimed assumptions (SCAs) on GitHub projects. To evaluate the applicability of SCAMiner, we first presented an example of using the tool to mine SCAs from one large and popular deep learning framework project: the TensorFlow project on GitHub. We then conducted an evaluation of the tool. The results show that SCAMiner can effectively identify and extract SCAs from the repositories on GitHub.