Recognizing Faults in Software Related Difficult Data

Abstract

In this paper we have investigated the use of numerous machine learning algorithms, with emphasis on multilayer artificial neural networks in the domain of software source code fault prediction. The main contribution lies in enhancing the data pre-processing step as the partial solution for handling software related difficult data. Before we put the data into an Artificial Neural Network, we are implementing PCA (Principal Component Analysis) and k-means clustering. The data-clustering step improves the quality of the whole dataset. Using the presented approach we were able to obtain 10% increase of accuracy of the fault detection. In order to ensure the most reliable results, we implement 10-fold cross-validation methodology during experiments. We have also evaluated a wide range of hyperparameter setups for the network, and compared the results to the state of the art, cost-sensitive approaches - Random Forest, AdaBoost, RepTrees and GBT.