Improving Reliability of Defect Prediction Models. From Temporal Reasoning and Machine Learning Perspective

      Аннотация: Developing error free software is a prime objective of software developers. However, achieving this objective is not a trivial task. This becomes even more harder for the development organizations with minimum resources. The developers often use automatic defect prediction models, which are mostly developed using machine learning algorithms, to locate defects in software. The prediction quality of such models is important as wrong predictions may negatively impact on the development organizations as well as the end users. This book explores the possible issues in the existing prediction models and proposes methods to further enhance the prediction quality of such models.

Ключевые слова: Defect prediction, Mining Software Repositories, Machine Learning Models