activity
20202022
most citedUnderstanding the Nature of System-Related Issues in Machine Learning Frameworks: An Exploratory Study

3 citations · 8 across the 6 of their papers we have counts for

collaborators

7 papers

cs.NI20223 cited

AI-Empowered Data Offloading in MEC-Enabled IoV Networks

Afonso Fontes, Igor de L. Ribeiro, Khan Muhammad +3

Advancements in smart vehicle design have enabled the creation of Internet of Vehicle (IoV) technologies that can utilize the information provided by various sensors and wireless c…

cs.SE20222 cited

Non-Functional Requirements for Machine Learning: An Exploration of System Scope and Interest

Khan Mohammad Habibullah, Gregory Gay, Jennifer Horkoff

Systems that rely on Machine Learning (ML systems) have differing demands on system quality compared to traditional systems. Such quality demands, known as non-functional requireme…

cs.SE2021

Automated Support for Unit Test Generation: A Tutorial Book Chapter

Afonso Fontes, Gregory Gay, Francisco Gomes de Oliveira Neto +1

Unit testing is a stage of testing where the smallest segment of code that can be tested in isolation from the rest of the system - often a class - is tested. Unit tests are typica…

cs.AI2021

Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVL

Hamid Ebadi, Mahshid Helali Moghadam, Markus Borg +3

With the growing capabilities of autonomous vehicles, there is a higher demand for sophisticated and pragmatic quality assurance approaches for machine learning-enabled systems in…

cs.SE2021

Using Machine Learning to Generate Test Oracles: A Systematic Literature Review

Afonso Fontes, Gregory Gay

Machine learning may enable the automated generation of test oracles. We have characterized emerging research in this area through a systematic literature review examining oracle t…

cs.SE2021

Learning How to Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection

Hussein Almulla, Gregory Gay

Search-based test generation is guided by feedback from one or more fitness functions - scoring functions that judge solution optimality. Choosing informative fitness functions is…