Overview of existing safeguarding techniques for automatically generated code

Stürmer, Ingo; Weinberg, Daniela; Conrad, Mirko

doi:10.1145/1082983.1083192

Cited by 19 publications

(9 citation statements)

References 6 publications

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“…A reliable and acceptable way to increase the confidence in the correctness of a code generator family is to validate and check the functionality of generated code, which is a common practice for compiler validation and testing [11,25,26]. Therefore, developers try to check the syntactic and semantic correctness of the generated code by means of different techniques such as static analysis, test suites, etc., and ensure that the code is behaving correctly.…”

Section: Functional Correctness Of a Code Generator Familymentioning

confidence: 99%

Automatic non-functional testing of code generators families

et al. 2016

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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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Section: Functional Correctness Of a Code Generator Familymentioning

confidence: 99%

Automatic non-functional testing of code generators families

et al. 2016

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“…In contrast to these rather analytical techniques, constructive approaches postulate a more systematic development process, e.g. based on the adoption of accepted standards [SWC05] or the generation of code from specifications [CG05].…”

Section: Related Workmentioning

confidence: 99%

Assuring property conformance of code generators via model checking

2011

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Abstract. Automatic code generation is an essential cornerstone of today's model-driven approaches to software engineering. Thus a key requirement for the success of this technique is the reliability and correctness of code generators. This article describes how we employ standard model checking-based verification to check that code generator models developed within our code generation framework Genesys conform to (temporal) properties. Genesys is a graphical framework for the high-level construction of code generators on the basis of an extensible library of well-defined building blocks along the lines of the Extreme Model-Driven Development (XMDD) paradigm. We will illustrate our verification approach by examining complex constraints for code generators, which even span entire model hierarchies. We also show how this leads to a knowledge base of rules for code generators, which we constantly extend by e.g. combining constraints to bigger constraints, or by deriving common patterns from structurally similar constraints. In our experience, the development of code generators with Genesys boils down to re-instantiating patterns or slightly modifying the graphical process model, activities which are strongly supported by verification facilities presented in this article.

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“…Not surprisingly, this imposes several challenges on the code-generating tools that exploit commercial compilers to provide the means for designers to tailor the code generator to a specific compiler suite. Note that the correctness of the generated code should not be compromised by excessive optimizations, and code-generator vendors should consider mechanisms 3,4 to ensure that. Returning to the PIL test, the resulting object code links with other test-management functionality and is then downloaded, typically to an off-the-shelf evaluation board of the target processor.…”

Section: Processor-in-the-loopmentioning

confidence: 99%