An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks

arXiv:2401.03653v3 Announce Type: replace-cross
Abstract: Stakeholders constantly make assumptions in the development of deep learning (DL) frameworks. These assumptions are related to various types of software artifacts (e.g., requirements, design decisions, and technical debt) and can turn out to be invalid, leading to system failures. Existing approaches and tools for assumption management usually depend on manual identification of assumptions. However, assumptions are scattered in various sources (e.g., code comments, commits, pull requests, and issues) of DL framework development, and manually …

abstract arxiv assumptions cs.lg cs.se debt decisions deep learning deep learning frameworks design development exploratory frameworks identification requirements software stakeholders study technical type types