SUMMARYReuse in object-oriented languages typically focuses on inheritance. Numerous techniques have been developed to provide finer-grained reuse of methods, such as flavors, mixins, and traits. These techniques, however, only deal with reuse at the level of classes. Class-based reuse is inherently static. Increasing use of reflection and meta-programming techniques in real-world applications underlines the need for more dynamic approaches. New approaches have shifted to object-specific reuse. However, these techniques fail to provide a complete solution to the composition issues arising during reuse. We propose a new approach that deals with reuse at the object level and that supports behavioral and state composition. We introduce a new abstraction called a talent that models features that are shared between objects of different class hierarchies. Talents provide a composition mechanism that is as flexible as that of traits but that is dynamic. Copyright

International audienceA feature represents a functional requirement fulfilled by a system. Since many maintenance tasks are expressed in terms of features, it is important to establish the correspondence between a feature and its implementation in source code. Traditional approaches to establish this correspondence exercise features to generate a trace of runtime events, which is then processed by post-mortem analysis. These approaches typically generate large amounts of data to analyze. Due to their static nature, these approaches do not support incremental and interactive analysis of features. We propose a radically different approach called live feature analysis, which provides a model at runtime of features. Our approach analyzes features on a running system and also makes it possible to grow feature representations by exercising different scenarios of the same feature, and identifies execution elements even to the sub-method level. We describe how live feature analysis is implemented effectively by annotating structural representations of code based on abstract syntax trees. We illustrate our live analysis with a case study where we achieve a more complete feature representation by exercising and merging variants of feature behavior and demonstrate the efficiency or our technique with benchmarks

Domain-specific languages and models are increasingly used within general-purpose host languages. While traditional profiling tools perform well on host language code itself, they often fail to provide meaningful results if the developers start to build and use abstractions on top of the host language. In this paper we motivate the need for dedicated profiling tools with three different case studies. Furthermore, we present an infrastructure that enables developers to quickly prototype new profilers for their domain-specific languages and models.

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