Exploring the integration of large language models in industrial test maintenance processes
Artikel i vetenskaplig tidskrift, 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
Författare
Jingxiong Liu
Göteborgs universitet
Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering
Ericsson AB
Ludvig Lemner
Student vid Chalmers
Ericsson AB
Linnea Wahlgren
Ericsson AB
Student vid Chalmers
Gregory Gay
Göteborgs universitet
Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering
Nasser Mohammadiha
Göteborgs universitet
Ericsson AB
Chalmers, Data- och informationsteknik, Data Science
Joakim Wennerberg
Ericsson AB
Journal of Systems and Software
0164-1212 (ISSN)
Vol. 242 113049Ämneskategorier (SSIF 2025)
Programvaruteknik
DOI
10.1016/j.jss.2026.113049