Improving Web Element Localization by Using a Large Language Model
Problem
Web-based test automation requires accurately locating web elements, but traditional attribute-matching methods miss context and meaning of elements and words.
Why it matters
Element localization accuracy directly determines the reliability of automated web testing.
What the AI did
VON Similo LLM uses a large language model to select the most likely web element from top-ranked candidates produced by the existing VON Similo method, evaluated on 804 element pairs from 48 real-world web applications.
Results
Correctly identified elements and execution times were measured on 804 pairs from 48 apps; values not stated in source
Impact
Peer-reviewed experimental study of LLM-based element selection; improvement magnitude not verifiable from abstract.
Record
| Venue | Software Testing Verification and Reliability |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Michel Nass, Emil Alégroth, ROBERT H. FELDT |
| Citations | 16 (OpenAlex, at import time) |
| References | 53 |
| Model or method | VON Similo LLM (LLM-based; GPT-4 cited as context) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1002/stvr.1893 |
| Open access | hybrid |
| Keywords | web element localization, LLM, test automation, GPT-4, software testing |
Importance rating
incremental / niche
Element localization accuracy affects reliability of automated web testing and maintenance costs; a genuine but niche software engineering improvement.
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