@Article{TrontoSilvSant:2008:InArNe,
               author = "Tronto, Iris Fabiana de Barcelos and Silva, Jos{\'e} 
                         Dem{\'{\i}}sio Sim{\~o}es da and Sant'Anna, Nilson",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "An investigation of artificial neural networks based prediction 
                         systems in software project management",
              journal = "Journal of Systems and Software",
                 year = "2008",
               volume = "81",
               number = "3",
                pages = "356--367",
                month = "Mar.",
             keywords = "software effort estimation, predictive accuracy, artificial neural 
                         networks, linear regression, data mining.",
             abstract = "A critical issue in software project management is the accurate 
                         estimation of size, effort, resources, cost, and time spent in the 
                         development process. Underestimates may lead to time pressures 
                         that may compromise full functional development and the software 
                         testing process. Likewise, overestimates can result in 
                         noncompetitive budgets. In this paper, artificial neural network 
                         and stepwise regression based predictive models are investigated, 
                         aiming at offering alternative methods for those who do not 
                         believe in estimation models. The results presented in this paper 
                         compare the performance of both methods and indicate that these 
                         techniques are competitive with the APF, SLIM, and COCOMO 
                         methods.",
                  doi = "10.1016/j.jss.2007.05.011",
                  url = "http://dx.doi.org/10.1016/j.jss.2007.05.011",
                 issn = "0164-1212",
             language = "en",
           targetfile = "an investigation.pdf",
        urlaccessdate = "16 jun. 2024"
}