On a Factorial Knowledge Architecture for Data Science-powered Software Engineering.

摘要: Given the data-intensive and collaborative trend in science, software engineering community also pays increasing attention to obtaining valuable useful insights from data repositories. Nevertheless, applying science (e.g., mining repositories) can be blindfold meaningless, if lacking a suitable knowledge architecture (KA). By observing that practices are generally recorded through set of factors programmer capacity, different environmental conditions, etc.) involved various project aspects, we propose factor-based hierarchical KA help maximize value repositories inspire future data-driven studies. In particular, it is organized their relationships guide mining, while mined will turn indexed/managed relevant interactions. This paper explains our idea about factorial concisely demonstrates component, i.e. early-version product engineering. Once fully scoped, this proposed supplement well-known SWEBOK terms both factor-centric management coverage/implication potential knowledge.