The prediction of defects in a target project based on data from external projects is called Cross-Project Defect Prediction (CPDP). Several methods have been proposed to improve the predictive performance of CPDP models. However, there is a lack of comparison among state-of-the-art methods. Moreover, previous work has shown that the most suitable method for a project can vary according to the project being predicted. This makes the choice of which method to use difficult. We provide an extensive experimental comparison of 31 CPDP methods derived from state-of-the-art approaches, applied to 47 versions of 15 open source software projects. Four methods stood out as presenting the best performances across datasets. However, the most suitable among these methods still varies according to the project being predicted. Therefore, we propose and evaluate a meta-learning solution designed to automatically select and recommend the most suitable CPDP method for a project. Our results show that the meta-learning solution is able to learn from previous experiences and recommend suitable methods dynamically. When compared to the base methods, however, the proposed solution presented minor difference of performance. These results provide valuable knowledge about the possibilities and limitations of a meta-learning solution applied for CPDP.
The goal of science is conclusion stability, i.e. to discover some effect X that holds in multiple situations. Sadly, there are all too few examples of stable conclusions in software engineering (SE). In fact, the typical result is conclusion instability where what is true for project one, does not hold for project two. We can find numerous studies of the following form: there is as much evidence for as against the argument that some aspect X adds value to a software project. Below are four examples of this type of problem which we believe to be endemic within SE. This paper reviews the supervised learning versions of the no-free-lunch theorems in a simplified form. It also discusses the significance of those theorems, and their relation to other aspects of supervised learning.