Automated Test Case Generation From Natural Language Requirements Using Natural Language Processing
Clicks: 1
ID: 312700
2025
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #324 of 705 articles by views in Journal of Computing & Biomedical Informatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Software testing is a vital process in the assurance of reliability and accuracy of the software systems, but manual testing is a slow, tedious, and error-prone process. The generation of test cases is a promising way, and currently, the existing approaches are frequently constrained by uncertainties in terms of natural language demands, low adaptability, and the lack of scalability. To alleviate such difficulties, this study proposes an end-to-end model based on Natural Language Processing (NLP), contextual embedding of BERT, and machine learning for automatic translation of unstructured requirements into structured and verifiable test cases. The proposed pipeline then involves requirement preprocessing, ambiguity detection, validity assessment, semantic representation, and classification, and then any structured test case documentation is done to assure traceability and completeness. The framework was tested on the DAMIR dataset and compared with the state-of-the-art methods, such as the scatter search, the NLP-based requirements formalization, and the generation of acceptance test cases based on NLP. Experimental outcomes demonstrate that the proposed model had high performance with an accuracy of between 92% and 97% and a maximum accuracy of 96.69, which is much higher than the current methods. Accuracy, recall, and F1-scores also confirmed the soundness of the framework in categorizing valid and invalid requirements. This paper illustrates how NLP-based test automation can be used to scale agile and continuous integration environments, to increase the reliability of the testing process, and to reduce human involvement. It provides the framework for future studies in multilingual requirement processing, domain-specific applications, and integration with large language models to enable greater flexibility.
| Reference Key |
imported_1777056119_69ebb977f19d9
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Summair Raza |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2025 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.