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Automatic Detecting Code Cooperation

Authors: Lei Wu 0001; Houari A. Sahraoui; Petko Valtchev;

Automatic Detecting Code Cooperation

Abstract

Software functionalities and behavior are accomplished by the cooperation of code artifacts. The understanding of this type of source code collaboration provides an important aid to the maintenance and evolution of legacy systems. However, the original collaboration design information is dispersed at the implementation level. The extraction of code artifacts' collaborations and the roles is therefore an important support in legacy software comprehension and design recovery. In this paper, we present a novel approach to automatically detect and analyze code collaborations and roles based on dynamic program analysis technique. We also demonstrate the tools that we have developed to support our approach and illustrate the viability of our approach in a case study.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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