# Natural Language Requirements Testability Measurement Based on Requirement Smells

arXiv: https://arxiv.org/abs/2403.17479 · HTML: https://mlsanity.com/paper/2403.17479/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 88 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Rapid quality assurance with Requirements Smells](https://mlsanity.com/paper/1611.08847/) (2016)
- [A survey on software testability](https://mlsanity.com/paper/1801.02201/) (2018)
- [An ensemble meta-estimator to predict source code testability](https://mlsanity.com/paper/2208.09614/) (2022)
- [Semantic Folding Theory And its Application in Semantic Fingerprinting](https://mlsanity.com/paper/1511.08855/) (2015)
- [Can clone detection support quality assessments of requirements specifications?](https://mlsanity.com/paper/1711.05472/) (2017)

## Built on by (most cited first, 1 of 1)

- [Specification-Based Testing of Large Language Models](https://mlsanity.com/paper/2610.14443/) (2026)

## Abstract (arXiv)

Requirements form the basis for defining software systems' obligations and tasks. Testable requirements help prevent failures, reduce maintenance costs, and make it easier to perform acceptance tests. However, despite the importance of measuring and quantifying requirements testability, no automatic approach for measuring requirements testability has been proposed based on the requirements smells, which are at odds with the requirements testability. This paper presents a mathematical model to evaluate and rank the natural language requirements testability based on an extensive set of nine requirements smells, detected automatically, and acceptance test efforts determined by requirement length and its application domain. Most of the smells stem from uncountable adjectives, context-sensitive, and ambiguous words. A comprehensive dictionary is required to detect such words. We offer a neural word-embedding technique to generate such a dictionary automatically. Using the dictionary, we could automatically detect Polysemy smell (domain-specific ambiguity) for the first time in 10 application domains. Our empirical study on nearly 1000 software requirements from six well-known industrial and academic projects demonstrates that the proposed smell detection approach outperforms Smella, a state-of-the-art tool, in detecting requirements smells. The precision and recall of smell detection are improved with an average of 0.03 and 0.33, respectively, compared to the state-of-the-art. The proposed requirement testability model measures the testability of 985 requirements with a mean absolute error of 0.12 and a mean squared error of 0.03, demonstrating the model's potential for practical use.