An empirical analysis of data preprocessing for machine learning-based software cost estimation

Context: Due to the complex nature of software development process, traditional parametric models and statistical methods often appear to be inadequate to model the increasingly complicated relationship between project development cost and the project features (or cost drivers). Machine learning (ML) methods, with several reported successful applications, have gained popularity for software cost estimation in recent years. Data preprocessing has been claimed by many researchers as a fundamental stage of ML methods; however, very few works have been focused on the effects of data preprocessing techniques. Objective: This study aims for an empirical assessment of the effectiveness of data preprocessing techniques on ML methods in the context of software cost estimation. Method: In this work, we first conduct a literature survey of the recent publications using data preprocessing techniques, followed by a systematic empirical study to analyze the strengths and weaknesses of individual data preprocessing techniques as well as their combinations. Results: Our results indicate that data preprocessing techniques may significantly influence the final prediction. They sometimes might have negative impacts on prediction performance of ML methods. Conclusion: In order to reduce prediction errors and improve efficiency, a careful selection is necessary according to the characteristics of machine learning methods, as well as the datasets used for software cost estimation. (C) 2015 Elsevier B.V. All rights reserved.