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Vector Abstraction and Concretization for Scalable Detection of Refactorings (A Technical Report)

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dc.contributor.author MILEA, Narcisa Andreea en_US
dc.contributor.author JIANG, Lingxiao en_US
dc.contributor.author KHOO, Siau-Cheng en_US
dc.date.accessioned 2014-06-04T07:04:25Z en_US
dc.date.accessioned 2017-01-23T07:00:08Z
dc.date.available 2014-06-04T07:04:25Z en_US
dc.date.available 2017-01-23T07:00:08Z
dc.date.issued 2014-03-28 en_US
dc.identifier.uri http://hdl.handle.net/1900.100/4625 en_US
dc.description.abstract Automated techniques have been proposed to either identify refactoring opportunities (i.e., code fragments that can, but have not yet been restructured in a program), or reconstruct historical refactoring (i.e., code restructuring operations that have happened between di erent versions of a program). However, it remains challenging to apply those techniques to large code bases containing millions of lines of code involving many versions. In this paper, we propose a new scalable technique that can be used for both identifying refactoring opportunities and historical refactoring. The key of our technique is the design of vector abstraction and concretization operations that can capture the essential patterns of the code changes induced by various refactoring operations in the form of characteristic vectors. Thus, the problem of identifying refactorings can be reduced to the problem of identifying matching vectors, which can be solved e ciently. We have implemented our technique for Java. We have applied the prototype to 200 bundle projects from the Eclipse ecosystem containing 4.5 million lines of code, and reports in total more than 32K instances of 17 types refactoring opportunities for all Eclipse projects, taking 25 minutes on average for each type. We have also applied the prototype to 14 versions of 3 smaller programs (JMeter, Ant, XML-Security), and detected (1) more than 2.8K refactoring opportunities within individual versions with an accuracy of about 87%, and (2) more than 190 historical refactorings across consecutive versions of the programs with an accuracy of about 92%. en_US
dc.language.iso en en_US
dc.relation.ispartofseries ;TRC3/2014 en_US
dc.title Vector Abstraction and Concretization for Scalable Detection of Refactorings (A Technical Report) en_US
dc.type Thesis en_US

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