Test-Data Generation for Xtext

Härtel, Johannes; Härtel, Lukas; Lämmel, Ralf

doi:10.1007/978-3-319-11245-9_19

Cited by 8 publications

(9 citation statements)

References 14 publications

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“…Similarly, in many design and verification tools used for engineering complex CPSs, system models are also represented internally as graphs, and model generators may be used for validating, testing or benchmarking such design tools [5][6][7][8]. As a main practical motivation for this scenario, while tool qualification of design and verification tools are necessitated by safety standards (like DO-178C [9], or ISO 26262 [10]), tool qualification is extremely costly due to the lack of effective best practices for validating the tools themselves.…”

Section: Introductionmentioning

confidence: 99%

“…(4) We integrate shape analysis as state encoding [29][30][31] for graphs to efficiently detect if two partial models should be treated as equivalent during exploration. (5) We exploit rule-based design space exploration [32] to drive the generation process directly over graph shapes using an objective function approximating the distance from a solution. (6) We evaluate the scalability of our approach using 6 tests sets of four domains (including industrial DSLs) and compare its performance with the well-known Alloy Analyzer [21].…”

Section: Introductionmentioning

confidence: 99%

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A graph solver for the automated generation of consistent domain-specific models

Semeráth

Nagy

Varró

2018

Proceedings of the 40th International Conference on Software Engineering

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Many testing and benchmarking scenarios in software and systems engineering depend on the systematic generation of graph models. For instance, tool qualification necessitated by safety standards would require a large set of consistent (well-formed or malformed) instance models specific to a domain. However, automatically generating consistent graph models which comply with a metamodel and satisfy all well-formedness constraints of industrial domains is a significant challenge. Existing solutions which map graph models into first-order logic specification to use back-end logic solvers (like Alloy or Z3) have severe scalability issues. In the paper, we propose a graph solver framework for the automated generation of consistent domain-specific instance models which operates directly over graphs by combining advanced techniques such as refinement of partial models, shape analysis, incremental graph query evaluation, and rule-based design space exploration to provide a more efficient guidance. Our initial performance evaluation carried out in four domains demonstrates that our approach is able to generate models which are 1-2 orders of magnitude larger (with 500 to 6000 objects!) compared to mapping-based approaches natively using Alloy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A graph solver for the automated generation of consistent domain-specific models

Semeráth

Nagy

Varró

2018

Proceedings of the 40th International Conference on Software Engineering

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“…There are some tools that use those coverage criteria to generate test cases, for example the YouGen [14], XTextGen [13] and LGen [27] tools. These tools…”

Section: Grammar-based Testingmentioning

confidence: 99%

“…In the same direction, the XTextGen [13] generates test-data based from grammars. This tool has a different approach to generate the data.…”

Section: Related Workmentioning

confidence: 99%

Reducing the Cost of Grammar-Based Testing Using Pattern Coverage

Hentz

Vinju

Moreira

2015

Testing Software and Systems

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Abstract. In grammar-based testing, context-free grammars may be used to generate relevant test inputs for language processors, or meta programs, such as programming language compilers, refactoring tools, and implementations of software quality metrics. This technique can be used to test these meta programs, but the amount of sentences, and syntax trees thereof, which needs to be generated to obtain reasonable coverage of the input language is exponential. Pattern matching is a programming language feature used often when writing meta programs. Pattern matching helps because it automates the frequently occurring task of detecting shapes in, and extracting information from syntax trees. However, meta programs which contain many patterns are difficult to test using only randomly generated sentences from grammar rules. The reason is that statistically it is uncommon to directly generate sentences which accidentally match the patterns in the code. To solve this problem, in this paper we extract information from the patterns in the code of meta programs to guide the sentence generation process. We introduce a new coverage criterion, called Pattern Coverage, which focuses on providing a test strategy to reduce the amount of test necessary cases, while covering the relevant parts of the meta program. An initial experimental evaluation is presented and the result is compared with traditional grammar-based testing.

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“…This is the author's version of the work. to be used for validating, testing or benchmarking MDE tools with advanced support for queries and transformations [18,10,2].…”

Section: Introductionmentioning

confidence: 99%

Towards the characterization of realistic models

Szárnyas

Kővári

Salánki

et al. 2016

Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems

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Custom generators of graph-based models are used in MDE for many purposes such as functional testing and performance benchmarking of modeling environments to ensure the correctness and scalability of tools. However, while existing generators may generate large models in increasing size, these models are claimed to be simple and synthetic, which hinders their credibility for industrial and research benchmarking purposes. But how to characterize a realistic model used in software and systems engineering? This question is investigated in the paper by collecting over 17 different widely used graph metrics taken from other disciplines (e.g. network theory) and evaluating them on 83 instance models originating from six modeling domains. Our preliminary results show that certain metrics are similar within a domain, but differ greatly between domains, which makes them suitable input for future instance model generators to derive more realistic models.

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Test-Data Generation for Xtext

Cited by 8 publications

References 14 publications

A graph solver for the automated generation of consistent domain-specific models

A graph solver for the automated generation of consistent domain-specific models

Reducing the Cost of Grammar-Based Testing Using Pattern Coverage

Towards the characterization of realistic models

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