Defect Inflow Prediction in Large Software Projects

Performance of software projects can be improved by providing predictions of various project characteristics. The predictions warn managers with information about potential problems and provide them with the possibility to prevent or avoid problems. Large software projects are characterized by a large number of factors that impact the project performance, which makes predicting project characteristics difficult. This paper presents methods for constructing prediction models of trends in defect inflow in large software projects based on a small number of variables. We refer to these models as short-term prediction models and long-term prediction models. The short-term prediction models are used to predict the number of defects discovered in the code up to three weeks in advance, while the long-term prediction models provide the possibility of predicting the defect inflow for the whole project. The initial evaluation of these methods in a large software project at Ericsson shows that the models are sufficiently accurate and easy to deploy.