Detecting Clones Across Microsoft .NET Programming Languages

Abstract

The Microsoft .NET framework and its language family focus on multi-language development to support interoperability across several programming languages. The framework allows for the development of similar applications in different languages through the reuse of core libraries. As a result of such a multi-language development, the identification and traceability of similar code fragments (clones) becomes a key challenge. In this paper, we present a clone detection approach for the .NET language family. The approach is based on the Common Intermediate Language, which is generated by the .NET compiler for the different languages within the .NET framework. In order to achieve an acceptable recall while maintaining the precision of our detection approach, we define a set of filtering processes to reduce noise in the raw data. We show that these filters are essential for Intermediate Languagebased clone detection, without significantly affecting the precision of the detection approach. Finally, we study the quantitative and qualitative performance aspects of our clone detection approach. We evaluate the number of reported candidate clone-pairs, as well as the precision and recall (using manual validation) for several open source cross-language systems, to show the effectiveness of our proposed approach.

<i>CLCMiner</i>: Detecting Cross-Language Clones without Intermediates

“…They implemented a tool called C2D2 based on the CodeDOM library in the Microsoft .NET framework, which uses NRefactory Library to generate the Unified CodeDOM graph for both C# and VB.NET. Al-omari et al [10] present a clone detection approach for the .NET language family too, based on the Common Intermediate Language (CIL). It can detect cross-language clone pairs in C#, J#, and VB.NET.…”

“…A number of researchers [3], [10] have started to detect crosslanguage code clones too. However, their approaches are limited to detect clones in the .NET language family that share a common intermediate language.…”

LHDiff: A Language-Independent Hybrid Approach for Tracking Source Code Lines

“…The core of the algorithm uses a hash function to generate simhash values. Among various non-cryptographic hash functions we use Jenkin hash function since it shows better similarity preserving behaviour compared to other functions and also found effective in detecting nearmiss code fragments in other studies [1], [29], [30]. We generate a 64 bit simhash value for both context and content using the simhash algorithm [25].…”

Structural and Nominal Cross-Language Clone Detection

“…Existing single-language clone detection techniques are unable to effectively detect these sorts of cross-language clones. In this paper we propose a method to detect cross-language clones and demonstrate that it (1) finds cross-language clones that no existing method can detect; and (2) performs comparably to existing single-language clone detectors for finding clones within a corpus of single-language code sources. Therefore, our technique generalizes A JavaScript (left) and Java (right) clone pair setting the weight and inverse weight of a particle in a graphics application.…”

“…2). That initial work has either focused on clones across languages that share a common intermediate representation such as .NET [1,15] or has deviated from classical clone detection and taken a more restricted, natural languagebased approach, sometimes relying on assumptions that may not be met in real code [7,8]. None of that existing work would detect the clone examples given in Figs.…”