Abenezer Angamo

dblp:426/3227 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Software testing
automated testing
0.912025
BenGQL: An Extensible Benchmarking Framework for Automated GraphQL Testing · ASE 2025
Empirical software engineering
benchmarking
0.312025
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
YearPublicationVenuePosition
2025 BenGQL: An Extensible Benchmarking Framework for Automated GraphQL Testing
abstract
GraphQL 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
ASE1
2025 KrakQL: LLM-Guided Blind Introspection of GraphQL Schemas
Marcello Maugeri, Abenezer Angamo, Giampaolo Bella
SSBSE2