Methodology of Algorithm Engineering

TL;DR

This paper proposes a comprehensive methodological framework for algorithm engineering, integrating philosophical perspectives to improve knowledge exchange and validity assessment across sub-disciplines.

Contribution

It develops a unified research framework based on ontology, epistemology, and methodology to standardize and enhance algorithm engineering research.

Findings

Framework clarifies the scope of algorithm engineering.

Highlights importance of validity concerns.

Facilitates interdisciplinary knowledge transfer.

Abstract

Research on algorithms has drastically increased in recent years. Various sub-disciplines of computer science investigate algorithms according to different objectives and standards. This plurality of the field has led to various methodological advances that have not yet been transferred to neighboring sub-disciplines. The central roadblock for a better knowledge exchange is the lack of a common methodological framework integrating the perspectives of these sub-disciplines. It is the objective of this paper to develop a research framework for algorithm engineering. Our framework builds on three areas discussed in the philosophy of science: ontology, epistemology and methodology. In essence, ontology describes algorithm engineering as being concerned with algorithmic problems, algorithmic tasks, algorithm designs and algorithm implementations. Epistemology describes the body of knowledge of algorithm engineering as a collection of prescriptive and descriptive knowledge, residing in World 3 of Popper's Three Worlds model. Methodology refers to the steps how we can systematically enhance our knowledge of specific algorithms. The framework helps us to identify and discuss various validity concerns relevant to any algorithm engineering contribution. In this way, our framework has important implications for researching algorithms in various areas of computer science.