Analogy-based software quality prediction

Predicting the stability of object-oriented systems is an important and challenging task. Classical approaches to quality prediction perform some form of inductive inference starting from datasets of software items with known quality factor values and looking for typical features that discriminate the items regarding the quality factor. However, most of the effective methods for predictive model construction are based on the implicit hypothesis that the available samples are representative, which is rather strong. The approach we propose implements a similarity-based comparison principle: the quality factor (stability) of a given software item is estimated from the recorded stability of a set of other items that have been recognized as the most similar to that item among a larger set of items stored in a database. This approach is evaluated using the successive versions of the JDK API.