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Automatic Non-functional Testing of Code Generators Families

Mohamed Boussaa 1 Olivier Barais 1 Benoit Baudry 1 Gerson Sunyé 2, 3 
1 DiverSe - Diversity-centric Software Engineering
Inria Rennes – Bretagne Atlantique , IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL
2 AtlanModels - Modeling Technologies for Software Production, Operation, and Evolution
LINA - Laboratoire d'Informatique de Nantes Atlantique, Département informatique - EMN, Inria Rennes – Bretagne Atlantique
Abstract : The intensive use of generative programming techniques provides an elegant engineering solution to deal with the heterogeneity of platforms and technological stacks. The use of domain-specific languages for example, leads to the creation of numerous code generators that automatically translate highlevel system specifications into multi-target executable code. Producing correct and efficient code generator is complex and error-prone. Although software designers provide generally high-level test suites to verify the functional outcome of generated code, it remains challenging and tedious to verify the behavior of produced code in terms of non-functional properties. This paper describes a practical approach based on a runtime monitoring infrastructure to automatically check the potential inefficient code generators. This infrastructure, based on system containers as execution platforms, allows code-generator developers to evaluate the generated code performance. We evaluate our approach by analyzing the performance of Haxe, a popular high-level programming language that involves a set of cross-platform code generators. Experimental results show that our approach is able to detect some performance inconsistencies that reveal real issues in Haxe code generators.
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Submitted on : Saturday, September 17, 2016 - 12:21:34 AM
Last modification on : Wednesday, April 27, 2022 - 4:21:45 AM
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  • HAL Id : hal-01356849, version 1


Mohamed Boussaa, Olivier Barais, Benoit Baudry, Gerson Sunyé. Automatic Non-functional Testing of Code Generators Families. 15th International Conference on Generative Programming: Concepts & Experiences (GPCE 2016), Oct 2016, Amsterdam, Netherlands. pp.11. ⟨hal-01356849⟩



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