# What Works Better? A Study of Classifying Requirements

arXiv: https://arxiv.org/abs/1707.02358 · HTML: https://mlsanity.com/paper/1707.02358/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks](https://mlsanity.com/paper/2405.05135/) (2024)
- [The Few-shot Dilemma: Over-prompting Large Language Models](https://mlsanity.com/paper/2509.13196/) (2025)
- [A Machine Learning Approach for Hierarchical Classification of Software Requirements](https://mlsanity.com/paper/2302.12599/) (2023)
- [Classification of Natural Language Processing Techniques for Requirements Engineering](https://mlsanity.com/paper/2204.04282/) (2022)
- [How Effective are Generative Large Language Models in Performing Requirements Classification?](https://mlsanity.com/paper/2504.16768/) (2025)
- [An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks](https://mlsanity.com/paper/2401.03653/) (2024)

## Abstract (arXiv)

Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Naive Bayes for the sub-classification of NFRs.