Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Zeno Montolli

dblp:385/8167 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 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 · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing › API testing
REST API testing
0.812024
DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning · ASE 2024
Software testing
test generation
0.812024
DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning · ASE 2024
Software testing
black-box testing
0.212024
DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning · ASE 2024
Software testing
specification-based testing
0.212024
DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning · ASE 2024

Methods — techniques the papers use, named apart from their topics

deep reinforcement learning · 0.8OpenAPI Specification · 0.8
YearPublicationVenuePosition
2024 DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning
abstract
Automatically crafting test scenarios for REST APIs helps deliver more reliable and trustworthy web-oriented systems. However, current black-box testing approaches rely heavily on the information available in the API's formal documentation, i.e., the Open API Specification (OAS for short). While useful, the OAS mostly covers syntactic aspects of the API (e.g., producer-consumer relations between operations, input value properties, and additional constraints in natural language), and it lacks a deeper understanding of the API business logic. Missing semantics include implicit ordering (logic dependency) between operations and implicit input-value constraints. These limitations hinder the ability of black-box testing tools to generate truly effective test cases automatically.
Davide Corradini, Zeno Montolli, Michele Pasqua, Mariano Ceccato
ASE2