# SELM: Software Engineering of Machine Learning Models

arXiv: https://arxiv.org/abs/2103.11249 · HTML: https://mlsanity.com/paper/2103.11249/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 20 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Online Bayesian Goal Inference for Boundedly-Rational Planning Agents](https://mlsanity.com/paper/2006.07532/) (2020)
- [DAST Model: Deciding About Semantic Complexity of a Text](https://mlsanity.com/paper/1908.09080/) (2019)
- [KARB Solution: Compliance to Quality by Rule Based Benchmarking](https://mlsanity.com/paper/2007.05874/) (2020)

## Abstract (arXiv)

One of the pillars of any machine learning model is its concepts. Using software engineering, we can engineer these concepts and then develop and expand them. In this article, we present a SELM framework for Software Engineering of machine Learning Models. We then evaluate this framework through a case study. Using the SELM framework, we can improve a machine learning process efficiency and provide more accuracy in learning with less processing hardware resources and a smaller training dataset. This issue highlights the importance of an interdisciplinary approach to machine learning. Therefore, in this article, we have provided interdisciplinary teams' proposals for machine learning.