Assessment of data-driven bayesian networks in software effort prediction

• Main Author: Tierno, Ivan Alexandre Paiz
• Other Authors: Nunes, Daltro José
• Format: Others
• Language: English
• Published: 2013
• Subjects: Redes bayesianas; Aprendizagem : Maquina; Redes : Computadores; Engenharia : Software; Software effort prediction; Bayesian networks; Machine learning; Data mining
• Online Access: http://hdl.handle.net/10183/71952

Software prediction unveils itself as a difficult but important task which can aid the manager on decision making, possibly allowing for time and resources sparing, achieving higher software quality among other benefits. One of the approaches set forth to perform this task has been the application of machine learning techniques. One of these techniques are Bayesian Networks, which have been promoted for software projects management due to their special features. However, the pre-processing procedures related to their application remain mostly neglected in this field. In this context, this study presents an assessment of automatic Bayesian Networks (i.e., Bayesian Networks solely based on data) on three public data sets and brings forward a discussion on data pre-processing procedures and the validation approach. We carried out a comparison of automatic Bayesian Networks against mean and median baseline models and also against ordinary least squares regression with a logarithmic transformation, which has been recently deemed in a comprehensive study as a top performer with regard to accuracy. The results obtained through careful validation procedures support that automatic Bayesian Networks can be competitive against other techniques, but still need improvements in order to catch up with linear regression models accuracy-wise. Some current limitations of Bayesian Networks are highlighted and possible improvements are discussed. Furthermore, this study provides some guidelines on the exploration of data. These guidelines can be useful to any Bayesian Networks that use data for model learning. Finally, this study also confirms the potential benefits of feature selection in software effort prediction.