Identifying defective software components is an essential activity during software development which contributes to continuously improving the software quality. Since relatively numerous defects are due to violated software dependencies, coupling metrics could increase the performance of software defect prediction. Among various measures expressing the coupling between software components, the conceptual coupling metrics capture similarities based on the semantic information contained in the source code. We are introducing a new conceptual coupling based metric suite, named COMET, for software defect prediction. Experiments conducted on publicly available data sets, using both unsupervised and supervised learning models, emphasize that COMET metrics suite is superior to the software metrics widely used in the defect prediction literature.

Relational Association Rules (RARs) capture generic relations between attributes values in possibly large data sets. Due to their ability to uncover underlying semantically relevant patterns, they are of particular interest in data mining research and applicable in both unsupervised and supervised learning scenarios. With the aim of increasing the stability and expressiveness of the classical, non-gradual RARs, Gradual Relational Association Rules (GRARs) have been introduced. By generalizing the boolean relations to gradual relations, GRARs also capture the degrees to which generic relations are satisfied. In the current paper we introduce a new approach called DynGRAR (Dynamic Gradual Relational Association Rules Miner) for uncovering interesting GRARs in dynamic data sets which are incrementally extended with both new data instances and new data attributes. DynGRAR dynamically adjusts the set of all interesting GRARs. Through multiple experiments performed on publicly available software defect prediction data sets, we have evaluated DynGRAR versus applying the standard GRARs mining algorithm from scratch on the extended data. The results obtained emphasize the superior performance of the dynamic approach we propose.

Online data mining techniques are used to uncover relevant patterns in complex data which are dynamic by nature and thus continuously extended with real-time arriving data streams. Relational association rules (RARs), a data analysis and mining concept, extend the classical association rules so as to capture different relations between the attributes characterizing the data. This paper introduces a new Incremental Relational Association Rule Mining (IRARM) approach with the aim of progressively adapting the interesting relational association rules identified in a data set, when it is enlarged with new instances. We have experimentally evaluated IRARM on publicly available data sets. The reduction in mining time when using IRARM against mining from scratch emphasizes its efficiency in adapting the rules to real-time data extension.