On the use of clone detection for identifying crosscutting concern code

Bruntink, Magiel; Deursen, Arie van; Engelen, Remco van; Tourwé, Tom

doi:10.1109/tse.2005.114

Cited by 107 publications

(74 citation statements)

References 26 publications

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“…The most evident and significant (p-value=< 2.2 · 10 −16 ) difference occurs when a new pattern (C)lass is added: also in this case the number of changes is higher when considering the extended change. Moreover, in Tomcat it is possible to note that the amount of changes involving methods (AMI 5 , M, MI 6 ) is higher in the co-change than in the extended change, even though such a difference is not statistically significant. Differently from JHotDraw, for Tomcat implementation changes (I) tend to induce more extended changes than co-changes in the crosscutting.…”

Section: Resultsmentioning

confidence: 96%

“…Differently from JHotDraw, for Tomcat implementation changes (I) tend to induce more extended changes than co-changes in the crosscutting. This because Tomcat is a larger system, involving more developers, and thus the maintenance of classes impacted by pattern change is delayed and, above all, it might be performed by a developer different from who maintained 5 AMI=changes in Attributes, Method signatures, and Implementations 6 MI=changes in Method signatures and Implementations the pattern.…”

Section: Resultsmentioning

confidence: 99%

“…The slice is computed from an expression or a statement pointed out by a developer. Bruntink et al [5] propose the use of clone detection for the identification of crosscutting concerns, comparing the performance of different clone detection techniques, namely AST-based and token-based. Aspect mining using dynamic analysis has been proposed by Breu and Krinke [3]: the idea is to detect particular patterns occurring in an execution trace.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Relating the Evolution of Design Patterns and Crosscutting Concerns

Cerulo¹,

Penta²

2007

Seventh IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2007)

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Relating the Evolution of Design Patterns and Crosscutting Concerns

Cerulo¹,

Penta²

2007

Seventh IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2007)

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“…Some other approaches rely on clone detection techniques to detect scattered code in the form of crosscutting concerns: Bruntink [21,22,23] [24] analyse version archives for crosscutting concerns. They consider methods to be part of a crosscutting concern when they are changed together in the same transaction and additionally the changes are the same, that is a call to the same method is inserted.…”

Section: Related Workmentioning

confidence: 99%

Mining execution relations for crosscutting concerns

Krinke

2008

IET Softw.

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Aspect mining tries to identify crosscutting concerns in the code of existing systems and thus supports their adaption to an aspect-oriented design.A semi-automatic static aspect mining approach is described, where the program's control flow graphs are investigated for recurring execution patterns based on different constraints, such as the requirement that the patterns have to exist in different calling contexts. Two case studies done with the implemented tool show that many discovered candidates for crosscutting concerns are instances of delegation and should not be refactored into aspects. More generally, it is shown that aspect mining techniques need a way to distinguish between delegation and superimposed behaviour.

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“…Whilst that list covers the more common applications, similarity assessment is used in many other areas, too. Examples include finding similar bug fixes (Hartmann et al 2010), identifying cross-cutting concerns (Bruntink et al 2005), program comprehension (Maletic and Marcus 2001), code recommendation (Holmes and Murphy 2005), and example extraction (Moreno et al 2015).…”

Section: Introductionmentioning

confidence: 99%

A comparison of code similarity analysers

2017

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Copying and pasting of source code is a common activity in software engineering. Often, the code is not copied as it is and it may be modified for various purposes; e.g. refactoring, bug fixing, or even software plagiarism. These code modifications could affect the performance of code similarity analysers including code clone and plagiarism detectors to some certain degree. We are interested in two types of code modification in this study: pervasive modifications, i.e. transformations that may have a global effect, and local modifications, i.e. code changes that are contained in a single method or code block. We evaluate 30 code similarity detection techniques and tools using five experimental scenarios for Java source code. These are (1) pervasively modified code, created with tools for source code and bytecode obfuscation, and boiler-plate code, (2) source code normalisation through compilation and decompilation using different decompilers, (3) reuse of optimal configurations over different data sets, (4) tool evaluation using ranked-based measures, and (5) local + global code modifications. Our experimental results show that in the presence of pervasive modifications, some of the general textual similarity measures can offer similar performance to specialised code similarity tools, whilst in the presence of boiler-plate code, highly specialised source code similarity detection techniques and tools outperform textual similarity measures. Our study strongly validates the use of compilation/decompilation as a normalisation technique. Its use reduced false classifications to zero for three of the tools. Moreover, we demonstrate that optimal configurations are very sensitive to a specific data set. After directly applying optimal configurations derived from one data set to another, the tools perform poorly on the new data set. The code similarity analysers are thoroughly evaluated not only based on several well-known pair-based and query-based error measures but also on each specific type of pervasive code modification. This broad, thorough study is the largest in existence and potentially an invaluable guide for future users of similarity detection in source code.

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On the use of clone detection for identifying crosscutting concern code

Cited by 107 publications

References 26 publications

Relating the Evolution of Design Patterns and Crosscutting Concerns

Relating the Evolution of Design Patterns and Crosscutting Concerns

Mining execution relations for crosscutting concerns

A comparison of code similarity analysers

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