Software engineering is a tedious job that includes people, tight deadlines and limited budgets. Delivering what customer wants involves minimizing the defects in the programs. Hence, it is important to establish quality measures early on in the project life cycle. The main objective of this research is to analyze problems in software code and propose a model that will help catching those problems earlier in the project life cycle.

Our proposed model uses machine learning methods. Principal Component Analysis is used for dimensionality reduction, and Decision Tree, Multi Layer Perceptron and Radial Basis Functions are used for defect prediction. The experiments in this research are carried out with different software metric datasets that are obtained from real-life projects of three big software companies in Turkey. We can say that, the improved method that we proposed brings out satisfactory results in terms of defect prediction.