Who Should Test the Requirement? A Comparative Study on Requirements Classification for Assigning Test Teams using the Pre-Trained Models

Analyzing software requirements is a complex task, particularly for projects with a large volume of requirements, and when conducted manually, this task is time-consuming and prone to human errors. Moreover, once the software implements the requirements, it is essential to conduct tests to ensure the correct validation. Within a software institute, each new requirement can be assigned to a test team (teams 1 and 2) responsible for ensuring coverage by updating or creating test cases. There are instances in which a requirement is not assigned to either the team or is assigned to both. Each test team is tasked with validating a specific scope of requirements, making it crucial that each requirement is analyzed and validated by an appropriate test team. If a test team fails to validate a requirement within its scope, it can result in software vulnerability. To mitigate these issues, this paper described the use of pre-trained models, such as BERT, XLNet, and ELECTRA, to automate the process of requirement classification, thereby determining which test team should validate each new requirement. We compared the models based on accuracy, precision, recall, F1-Score, and Area Under Curve (AUC) macro metrics. Notably, the XLNet model demonstrated superior performance among the models, achieving 93.16% AUC Macro, while the BERT model achieved 91.28% AUC, and the ELECTRA model achieved 90.17% AUC.We also applied the non-parametric Friedman test to statistically validate the results, followed by the Conover squared rank test, with a significance level of 0.05. The results indicate that XLNet outperformed the BERT and ELECTRA models, exhibiting a superior capacity for assigning requirements to the correct test teams. Given the promising results of this research, this study aims to demonstrate the viability of using pre-trained models as a solution to optimize the testing process in the software industry.

Authors

Institutions

Publication Details

Published
2025-09-22
DOI
https://doi.org/10.5753/sbes.2025.11009
Citations
1
Primary Topic
Software Engineering Techniques and Practices
Type
article
Field-Weighted Citation Impact
3.25

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Who Should Test the Requirement? A Comparative Study on Requirements Classification for Assigning Test Teams using the Pre-Trained Models

Lennon Chaves, Flávia Oliveira, Leonardo Tiago, A. S. do Nascimento
1 citations
Software Engineering Techniques and Practices
3.25
article

Who Should Test the Requirement? A Comparative Study on Requirements Classification for Assigning Test Teams using the Pre-Trained Models

Lennon Chaves, Flávia Oliveira, Leonardo Tiago, A. S. do Nascimento
article en
1 citations

Abstract

Analyzing software requirements is a complex task, particularly for projects with a large volume of requirements, and when conducted manually, this task is time-consuming and prone to human errors. Moreover, once the software implements the requirements, it is essential to conduct tests to ensure the correct validation. Within a software institute, each new requirement can be assigned to a test team (teams 1 and 2) responsible for ensuring coverage by updating or creating test cases. There are instances in which a requirement is not assigned to either the team or is assigned to both. Each test team is tasked with validating a specific scope of requirements, making it crucial that each requirement is analyzed and validated by an appropriate test team. If a test team fails to validate a requirement within its scope, it can result in software vulnerability. To mitigate these issues, this paper described the use of pre-trained models, such as BERT, XLNet, and ELECTRA, to automate the process of requirement classification, thereby determining which test team should validate each new requirement. We compared the models based on accuracy, precision, recall, F1-Score, and Area Under Curve (AUC) macro metrics. Notably, the XLNet model demonstrated superior performance among the models, achieving 93.16% AUC Macro, while the BERT model achieved 91.28% AUC, and the ELECTRA model achieved 90.17% AUC.We also applied the non-parametric Friedman test to statistically validate the results, followed by the Conover squared rank test, with a significance level of 0.05. The results indicate that XLNet outperformed the BERT and ELECTRA models, exhibiting a superior capacity for assigning requirements to the correct test teams. Given the promising results of this research, this study aims to demonstrate the viability of using pre-trained models as a solution to optimize the testing process in the software industry.

Institute of Technology of Cambodia (KH), Institute of Science and Technology
Samsung Eletrônica da Amazônia, Samsung
Openalex Percentile: Top 6%
Software Engineering Techniques and Practices
3.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.