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), 
            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 = "28 nov. 2020"