# Methodology of Algorithm Engineering

arXiv: https://arxiv.org/abs/2310.18979 · HTML: https://mlsanity.com/paper/2310.18979/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2023.

## Built on by (most cited first, 2 of 2)

- [Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Prediction](https://mlsanity.com/paper/2312.08847/) (2023)
- [From Understanding to Excelling: Template-Free Algorithm Design through Structural-Functional Co-Evolution](https://mlsanity.com/paper/2503.10721/) (2025)

## Abstract (arXiv)

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.