Exploring the integration of large language models in industrial test maintenance processes
Journal article, 2026
Much of the cost and effort required during the software testing process is invested in performing test maintenance—the addition, removal, or modification of test cases to keep the test suite in sync with the system-under-test or to otherwise improve its quality. Tool support could reduce the cost—and improve the quality—of test maintenance by automating aspects of the process or by providing guidance and support to developers. In this study, we explore the capabilities and applications of large language models (LLMs)—complex machine learning models adapted to textual analysis—to support test maintenance. We conducted a case study at Ericsson AB where we explore the triggers that indicate the need for test maintenance, the actions that LLMs can take, and the considerations that must be made when deploying LLMs in an industrial setting. We also propose and demonstrate a multi-agent architecture that can predict which tests require maintenance following a change to the source code. Collectively, these contributions advance our understanding of how LLMs could be deployed to benefit industrial test maintenance processes.
Software testing
Machine learning
LLM agent
Large language models
Test maintenance
Author
Jingxiong Liu
University of Gothenburg
Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering
Ericsson
Ludvig Lemner
Student at Chalmers
Ericsson
Linnea Wahlgren
Ericsson
Student at Chalmers
Gregory Gay
University of Gothenburg
Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering
Nasser Mohammadiha
University of Gothenburg
Ericsson
Chalmers, Computer Science and Engineering (Chalmers), Data Science
Joakim Wennerberg
Ericsson
Journal of Systems and Software
0164-1212 (ISSN)
Vol. 242 113049Subject Categories (SSIF 2025)
Software Engineering
DOI
10.1016/j.jss.2026.113049