A Sentence Generation Algorithm for Testing Grammars

Zheng, Lixiao; Wu, Duanyi

doi:10.1109/compsac.2009.193

Cited by 12 publications

(11 citation statements)

References 8 publications

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“…The sentence generation algorithms provided in our toolkit are based on grammar coverage criteria, including rule coverage [5] and context-dependent rule coverage [2]. Purdom's algorithm [5] generates a small sentence set satisfying rule coverage, while an extension of Purdom's algorithm [6] builds upon Purdom's algorithm with a length control mechanism. For context-dependent rule coverage, similarly there are two algorithms, namely CDRC-P algorithm [8], based on Purdom's algorithm, and an extension of CDRC-P algorithm, extending CDRC-P algorithm with a length control mechanism.…”

Section: The Toolkit: An Overviewmentioning

confidence: 99%

“…• Manipulations of context-free grammars -Editing of context-free grammars -Checking the usefulness and reachability of nonterminals • Sentence generation with coverage criteria -Purdom's algorithm [5] -An extension of Purdom's algorithm [6] -CDRC-P algorithm [8] 1 In the paper, the height of a parse tree is defined as the maximum path length which is the number of nonterminals in a path traversed from the root to the leaf node.…”

Section: The Toolkit: An Overviewmentioning

confidence: 99%

“…These sentences are inadequate in many practical applications. To avoid this, an extended algorithm [6] is proposed and implemented which generates more and simpler sentences. A more precise criteria is context-dependent rule coverage [2].…”

Section: Introductionmentioning

confidence: 99%

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A Toolkit for Generating Sentences from Context-Free Grammars

Zheng

Chen

2010

2010 8th IEEE International Conference on Software Engineering and Formal Methods

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Producing sentences from a grammar, according to various criteria, is required in many applications. It is also a basic building block for grammar engineering. This paper presents a toolkit for context-free grammars, which mainly consists of several algorithms for sentence generation or enumeration and for coverage analysis for context-free grammars. The toolkit deals with general context-free grammars. Besides providing implementations of algorithms, the toolkit also provides a simple graphical user interface, through which the user can use the toolkit directly. The toolkit is implemented in Java and is available at http://lcs.ios.ac.cn/ ∼ zhiwu/toolkit.php. In the paper, the overview of the toolkit and the description of the GUI are presented, and experimental results and preliminary applications of the toolkit are also contained.

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Section: The Toolkit: An Overviewmentioning

confidence: 99%

Section: The Toolkit: An Overviewmentioning

confidence: 99%

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A Toolkit for Generating Sentences from Context-Free Grammars

Zheng

Chen

2010

2010 8th IEEE International Conference on Software Engineering and Formal Methods

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“…Although there is no detailed time complexity analysis for fast grammarbased test generation algorithms [56], [46], [66], the performance is generally polynomial in jP j, that is, OðjP j c Þ for some c ! 1.…”

Section: Positive Test Case Generationmentioning

confidence: 99%

Exploiting Model Morphology for Event-Based Testing

Belli

Beyazit

2015

IIEEE Trans. Software Eng.

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Abstract-Model-based testing employs models for testing. Model-based mutation testing (MBMT) additionally involves fault models, called mutants, by applying mutation operators to the original model. A problem encountered with MBMT is the elimination of equivalent mutants and multiple mutants modeling the same faults. Another problem is the need to compare a mutant to the original model for test generation. This paper proposes an event-based approach to MBMT that is not fixed on single events and a single model but rather operates on sequences of events of length k ! 1 and invokes a sequence of models that are derived from the original one by varying its morphology based on k. The approach employs formal grammars, related mutation operators, and algorithms to generate test cases, enabling the following: (1) the exclusion of equivalent mutants and multiple mutants; (2) the generation of a test case in linear time to kill a selected mutant without comparing it to the original model; (3) the analysis of morphologically different models enabling the systematic generation of mutants, thereby extending the set of fault models studied in related literature. Three case studies validate the approach and analyze its characteristics in comparison to random testing and another MBMT approach.

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“…Practically, a test case may easily blow out—becoming infinitely long—even if it is suggested that the probabilities of non‐recursive production rules should be much higher than those of recursive rules to avoid an infinite recursion . Hence, other constraints (e.g., length control in ), heuristics, or hints are often required to ensure the termination of generating test cases. For example, the lava tool takes a production grammar and a seed, which consists of a high‐level description that guides the production process, to generate test suites for Java virtual machine.…”

Section: Introductionmentioning

confidence: 99%

A dynamic stochastic model for automatic grammar‐based test generation

Guo

Qiu

2014

Softw Pract Exp

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SUMMARYGrammar-based test generation provides a systematic approach to producing test cases from a given contextfree grammar. Unfortunately, naive grammar-based test generation is problematic because of the fact that exhaustive random test case production is often explosive, and grammar-based test generation with explicit annotation controls often causes unbalanced testing coverage. In this paper, we present an automatic grammar-based test generation approach, which takes a symbolic grammar as input, requires zero control input from users, and produces well-distributed test cases. Our approach utilizes a novel dynamic stochastic model where each variable is associated with a tuple of probability distributions, which are dynamically adjusted along the derivation. We further present a coverage tree illustrating the distribution of generated test cases and their detailed derivations. More importantly, the coverage tree supports various implicit derivation control mechanisms. We implemented this approach in a Java-based system, named Gena. Each test case generated by Gena automatically comes with a set of structural features, which can play an important and effective role on automated failure causes localization. Experimental results demonstrate the effectiveness of our approach, the well-balanced distribution of generated test cases over grammatical structures, and a case study on grammar-based failure causes localization.

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A Sentence Generation Algorithm for Testing Grammars

Cited by 12 publications

References 8 publications

A Toolkit for Generating Sentences from Context-Free Grammars

A Toolkit for Generating Sentences from Context-Free Grammars

Exploiting Model Morphology for Event-Based Testing

A dynamic stochastic model for automatic grammar‐based test generation

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