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