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

TL;DR

This paper proposes a hierarchical, factor-based knowledge architecture for software engineering to enhance data-driven insights from software repositories, aiming to improve knowledge management and guide future research.

Contribution

It introduces a novel factorial knowledge architecture tailored for software engineering, emphasizing factors and their relationships for better knowledge mining and management.

Findings

Proposed a hierarchical factorial knowledge architecture for software engineering.

Demonstrated an initial version of the architecture for software product engineering.

Aims to complement SWEBOK with factor-centric knowledge management.

Abstract

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.