Code clone detection experience at microsoft

Dang, Yingnong; Song, Ge; Huang, Ray; Zhang, Dongmei

doi:10.1145/1985404.1985417

Cited by 28 publications

(18 citation statements)

References 2 publications

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“…4) The Microsoft prototype uses a Microsoft tool (referred to as XIAO) [32] [12] for code clone detection in the historical versions of C# projects, while the Open prototype uses ConQAT 10 , a scalable code-clone detector for multiple programming languages.…”

Section: Methodsmentioning

confidence: 99%

“…These techniques are the most scalable, but the least robust (can detect identical or near-identical clones but more sensitive to revisions after copy-and-paste). In our research, we used the CloneCodeDetector [32] [12] from Microsoft for code-clone detection in Microsoft projects, and Conqat [13] for the code-clone detection in opensource projects. Both code-clone-detection tools leverage a token-based technique, and they are stable and scalable enough for analyzing large software projects.…”

Section: Code Clone Detectionmentioning

confidence: 99%

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Predicting Consistency-Maintenance Requirement of Code Clonesat Copy-and-Paste Time

Wang¹,

Dang

Zhang

et al. 2014

IIEEE Trans. Software Eng.

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Abstract-Code clones have always been a double edged sword in software development. On one hand, it is a very convenient way to reuse existing code, and to save coding effort. On the other hand, since developers may need to ensure consistency among cloned code segments, code clones can lead to extra maintenance effort and even bugs. Recently studies on the evolution of code clones show that only some of the code clones experience consistent changes during their evolution history. Therefore, if we can accurately predict whether a code clone will experience consistent changes, we will be able to provide useful recommendations to developers on leveraging the convenience of some code cloning operations, while avoiding other code cloning operations to reduce future consistency maintenance effort. In this paper, we define a code cloning operation as consistency-maintenance-required if its generated code clones experience consistent changes in the software evolution history, and we propose a novel approach that automatically predicts whether a code cloning operation requires consistency maintenance at the time point of performing copy-and-paste operations. Our insight is that whether a code cloning operation requires consistency maintenance may relate to the characteristics of the code to be cloned and the characteristics of its context. Based on a number of attributes extracted from the cloned code and the context of the code cloning operation, we use Bayesian Networks, a machine-learning technique, to predict whether an intended code cloning operation requires consistency maintenance. We evaluated our approach on four subjects -two large-scale Microsoft software projects, and two popular open-source software projects -under two usage scenarios: 1) recommend developers to perform only the cloning operations predicted to be very likely to be consistency-maintenance-free, and 2) recommend developers to perform all cloning operations unless they are predicted very likely to be consistency-maintenance-required. In the first scenario, our approach is able to recommend developers to perform more than 50% cloning operations with a precision of at least 94% in the four subjects. In the second scenario, our approach is able to avoid 37% to 72% consistency-maintenance-required code clones by warning developers on only 13% to 40% code clones, in the four subjects.

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Section: Methodsmentioning

confidence: 99%

Section: Code Clone Detectionmentioning

confidence: 99%

Predicting Consistency-Maintenance Requirement of Code Clonesat Copy-and-Paste Time

Wang¹,

Dang

Zhang

et al. 2014

IIEEE Trans. Software Eng.

Self Cite

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“…Even supported by a visualization tool, it is unrealistic for developers to decide manually whether each clone class is appropriate for refactoring. The impracticality of analyzing and managing a prohibitively large volume of clones has also been discussed by industrial developers [7], [6].…”

Section: A Clone Refactoring In An Open Source Projectmentioning

confidence: 99%

Recommending Clones for Refactoring Using Design, Context, and History

Wang

Godfrey

2014

2014 IEEE International Conference on Software Maintenance and Evolution

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Abstract-Developers know that copy-pasting code (aka code cloning) is often a convenient shortcut to achieving a design goal, albeit one that carries risks to the code quality over time. However, deciding which, if any, clones should be eliminated within an existing system is a daunting task. "Fixing" a clone usually means performing an invasive refactoring, and not all clones may be worth the effort, cost, and risk that such a change entails. Furthermore, sometimes cloning fulfils a useful design role, and should not be refactored at all [23]. And clone detection tools often return very large result sets, making it hard to choose which clones should be investigated and possibly removed.In this paper, we propose an automated approach to recommend clones for refactoring by training a decision tree-based classifier. We analyze more than 600 clone instances in three medium-to large-sized open source projects, and we collect features that are associated with the source code, the context, and the history of clone instances. Our approach achieves a precision of around 80% in recommending clone refactoring instances for each target system, and similarly good precision is achieved in cross-project evaluation. By recommending which clones are appropriate for refactoring, our approach allows for better resource allocation for refactoring itself after obtaining clone detection results, and can thus lead to improved clone management in practice.

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“…[6] report their experience at Microsoft research using their XIAO code clone detection approach in a real development setting. Like us, the authors present the importance of presenting clone results to the engineers in an understandable and intuitive way, as well as the need for near-miss clone detection techniques with sorting and filtering capabilities, which will help engineers to identify clones of interest and eliminate any irrelevant clones.…”

Section: Related Work In Clone Pattern Visualizationmentioning

confidence: 99%

Analysis and clustering of model clones: An automotive industrial experience

Alalfi

Cordy

Dean

2014

2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE)

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Abstract-In this paper we present our early experience analyzing subsystem similarity in industrial automotive models. We apply our model clone detection tool, SIMONE, to identify identical and near-miss Simulink subsystem clones and cluster them into classes based on clone size and similarity threshold. We then analyze clone detection results using graph visualizations generated by the SIMGraph, a SIMONE extension, to identify subsystem patterns. SIMGraph provides us and our industrial partners with new interesting and useful insights that improves our understanding of the analyzed models and suggests better ways to maintain them.

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Code clone detection experience at microsoft

Cited by 28 publications

References 2 publications

Predicting Consistency-Maintenance Requirement of Code Clonesat Copy-and-Paste Time

Predicting Consistency-Maintenance Requirement of Code Clonesat Copy-and-Paste Time

Recommending Clones for Refactoring Using Design, Context, and History

Analysis and clustering of model clones: An automotive industrial experience

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