Combinatorial approach of association rule mining in software engineering

Abstract

Classification based on association rule mining aims to establish a model based on association rules, to classify an unknown object efficiently. In Software Engineering it improves software productivity and quality; software engineers are increasingly applying data mining algorithms to various software engineering tasks. However mining software engineering data poses several challenges, requiring various algorithms to effectively mine sequences, graphs and text from such data. Software engineering data includes code bases, execution traces, historical code changes, mailing lists and bug data bases. Data mining can be used in gathering and extracting latent security requirements, extracting algorithms and business rules from code, mining legacy applications for requirements and business rules for new projects etc. Mining algorithms for software engineering falls into four main categories: Frequent pattern mining - finding commonly occurring patterns; Pattern matching - finding data instances for given patterns; Clustering - grouping data into clusters and Classification - predicting labels of data based on already labeled data. In this paper, we propose a new approach of association rule mining named Combinatorial Approach of Association Rule Mining (CAARM) in software engineering. The CAARM makes use of combinatorial mathematics to discover all frequent patterns / itemsets in just a single scan of the training database. It also doesn't perform very complex or lengthy calculations during the process. Consequently, it mines all the association rules in the most efficient way.