Identification of Recurring Patterns of Code to Detect Structural Clones

Abstract

Code clones are code fragments identical to each other. Simple code clones are easily detected by automated clone detection tools. But huge numbers of simple clones are reported by these tools. Careful observation is required to extract useful information from this large number of simple clones. There are recurring patterns of simple clones which indicate design level similarities called structural clones. Structural clones can be detected from the co-located simple clones by applying data mining techniques. Integrated Architecture is proposed to detect structural clones form simple clones. There are different types of clones which come under category of higher level clones. In this paper, various higher level clone terminologies are highlighted and hierarchy of clones is formulated. Also Integrated architecture is proposed by combining data mining and simple code clone detection techniques. This will be helpful to determine search path to detect clones. Also three Data Mining Techniques: FIM (Frequent Item Set Mining), FCIM (Frequent Closed Item Set Mining), WFIM (weighted Frequent Item Set Mining) have been discussed. Among these three techniques, WFIM is efficient, because it further reduces number of frequent item sets of recurring patterns reported by FCIM.