EDBT 2026 Demo / reviewers in the wild / expert
Abenezer Angamo
dblp:426/3227
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2025
0009-0005-5707-3010ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 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 · 77% Empirical software engineering · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing
automated testing |
0.9 | 1 | 2025 | BenGQL: An Extensible Benchmarking Framework for Automated GraphQL Testing · ASE 2025 |
Empirical software engineering
benchmarking |
0.3 | 1 | 2025 | BenGQL: An Extensible Benchmarking Framework for Automated GraphQL Testing · ASE 2025 |
Methods — techniques the papers use, named apart from their topics
load testing · 0.9fuzzing · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | BenGQL: An Extensible Benchmarking Framework for Automated GraphQL TestingabstractGraphQL APIs provide a unified endpoint for retrieving and uploading data in a web application. Due to its efficient data-fetching strategy, which allows for the retrieval of only the required data, GraphQL is gaining popularity. Its software nature necessitates robust testing, both functional and non-functional. As automated testing tools, including load testers and fuzzers, are developed to assess GraphQL APIs, they lack a common set of case studies for rigorous evaluation and comparison.To address this gap, we present BenGQL, a benchmarking framework containing 23 representative open-source GraphQL server applications, spanning different underlying engines and schema complexities. BenGQL provides an extensible infrastructure for running testing tools against these case studies, enabling developers and researchers to: (i) execute testing tools against the same case studies, (ii) analyse and compare results using custom analysis modules, and (iii) extend the benchmark with new case studies, tools, or metrics.The ultimate goal of BenGQL is to foster more rigorous, reproducible research in automated GraphQL testing by providing both the case studies and the infrastructure for running experiments. As a consequence, we release the source code at https://github.com/marcellomaugeri/BenGQL, inviting other researchers to contribute. A video demonstration is also available at https://youtu.be/wZ06Xxa_Koo. Abenezer Angamo, Marcello Maugeri |
ASE | 1 |
| 2025 | KrakQL: LLM-Guided Blind Introspection of GraphQL Schemas
Marcello Maugeri, Abenezer Angamo, Giampaolo Bella |
SSBSE | 2 |