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Tracking Objects to Detect Feature Dependencies

Authors: Adrian Lienhard; Orla Greevy; Oscar Nierstrasz;

Tracking Objects to Detect Feature Dependencies

Abstract

The domain-specific ontology of a software system includes a set of features and their relationships. While the problem of locating features in object-oriented programs has been widely studied, runtime dependencies between features are less well understood. Features cannot be understood in isolation, since their behavior often depends on objects created and referenced in previously exercised features. It is difficult to spot runtime dependencies between features just by browsing source code. Hence, code modifications intended for one feature, often inadvertently affect other features. In this paper, we propose an approach to precisely identify dependencies between features based on a fine-grained dynamic analysis which captures details about how objects are referenced at runtime. The results of two case studies indicate that our approach helps software maintainers in understanding critical feature dependencies.

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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!
22
Top 10%
Top 10%
Top 10%
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