Demo Data from EDBT 2026 / reviewers in the wild / expert
Pedro Luís Fonseca
dblp:420/0995
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Software testing · 72% Program synthesis and code generation · 28% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing › software validation
acceptance testing |
0.9 | 1 | 2025 | Streamlining Acceptance Test Generation for Mobile Applications Through Large Language Models: An Industrial Case Study · ASE 2025 |
Program synthesis and code generation
code generation with language models |
0.9 | 1 | 2025 | Streamlining Acceptance Test Generation for Mobile Applications Through Large Language Models: An Industrial Case Study · ASE 2025 |
Software testing › test generation › automated test generation
test script generation |
0.9 | 1 | 2025 | Streamlining Acceptance Test Generation for Mobile Applications Through Large Language Models: An Industrial Case Study · ASE 2025 |
Software testing
test artifact generation |
0.3 | 1 | 2025 | Streamlining Acceptance Test Generation for Mobile Applications Through Large Language Models: An Industrial Case Study · ASE 2025 |
Software testing
test maintenance |
0.3 | 1 | 2025 | Streamlining Acceptance Test Generation for Mobile Applications Through Large Language Models: An Industrial Case Study · ASE 2025 |
Methods — techniques the papers use, named apart from their topics
page objects · 0.9large language model · 0.9gherkin · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Streamlining Acceptance Test Generation for Mobile Applications Through Large Language Models: An Industrial Case StudyabstractMobile acceptance testing remains a bottleneck in modern software development, particularly for cross-platform mobile development using frameworks like Flutter. While developers increasingly rely on automated testing tools, creating and maintaining acceptance test artifacts still demands significant manual effort. To help tackle this issue, we introduce AToMIC, an automated framework leveraging specialized Large Language Models to generate Gherkin scenarios, Page Objects, and executable UI test scripts directly from requirements (JIRA tickets) and recent code changes. Applied to BMW’s MyBMW app, covering 13 real-world issues in a 170+ screen codebase, AToMIC produced executable test artifacts in under five minutes per feature on standard hardware. The generated artifacts were of high quality: 93.3% of Gherkin scenarios were syntactically correct upon generation, 78.8% of PageObjects ran without manual edits, and 100% of generated UI tests executed successfully. In a survey, all practitioners reported time savings (often a full developer-day per feature) and strong confidence in adopting the approach. These results confirm AToMIC as a scalable, practical solution for streamlining acceptance test creation and maintenance in industrial mobile projects. Pedro Luís Fonseca, Bruno Lima, João Pascoal Faria |
ASE | 1 |