@InProceedings{SantiagoJúniorSilvAndr:2018:TeEnMo,
author = "Santiago J{\'u}nior, Valdivino Alexandre de and Silva, Leoni
Augusto Romain da and Andrade Neto, Pedro Ribeiro de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Testing environmental models supported by machine learning",
booktitle = "Proceedings...",
year = "2018",
organization = "Brazilian Symposium on Systematic and Automated Testing (CBSOFT),
3.",
publisher = "Association for Computing Machinery",
keywords = "Combinatorial Interaction Testing, Model-Based Testing, Random
Testing, Machine Learning, Environmental Modeling, Empirical
Software Engineering, Digital Image Processing.",
abstract = "In this paper we present a new methodology, DaOBML, to test
environmental models whose outputs are complex artifacts such as
images (maps) or plots. Our approach suggests several test data
generation techniques (Combinatorial Interaction Testing,
ModelBased Testing, Random Testing) and digital image processing
methods to drive the creation of Knowledge Bases (KBs).
Considering such KBs and Machine Learning (ML) algorithms, a test
oracle assigns the verdicts of new test data. Our methodology is
supported by a tool and we applied it to models developed via the
TerraME product. A controlled experiment was carried out and we
conclude that Random Testing is the most feasible test data
generation approach for developing the KBs, Artificial Neural
Networks present the best performance out of six ML algorithms,
and the larger the KB, in terms of size, the better.",
conference-location = "S{\~a}o Carlos, SP",
conference-year = "17-21 set.",
doi = "10.1145/3266003.3266004",
url = "http://dx.doi.org/10.1145/3266003.3266004",
isbn = "978-145036555-0",
language = "en",
targetfile = "santiago_testing.pdf",
urlaccessdate = "13 maio 2024"
}