Grammar-based test generators are highly efficient in producing syntactically valid test inputs, and give their user precise control over which test inputs should be generated. Adapting a grammar or a test generator towards a particular testing goal can be tedious, though. We introduce the concept of a grammar transformer, specializing a grammar towards inclusion or exclusion of specific patterns: “The phone number must not start with 011 or +1”. To the best of our knowledge, ours is the first approach to allow for arbitrary Boolean combinations of patterns, giving testers unprecedented flexibility in creating targeted software tests. The resulting specialized grammars can be used with any grammar-based fuzzer for targeted test generation, ...
peer reviewedGrammars can serve as producers for structured test inputs that are syntactically corre...
Grammar-based test generation provides a systematic approach to producing test cases from a given co...
In this paper, we present an automatic grammar-based test generation approach which takes a symbolic...
In grammar-based testing, context-free grammars may be used to generate relevant test inputs for lan...
Grammar-based fuzzing is an effective method for testing programs that consume structured inputs, an...
In grammar-based testing, context-free grammars may be used to generate relevant test inputs for lan...
Part 2: Test Derivation MethodsInternational audienceIn grammar-based testing, context-free grammars...
Covering arrays and context-free grammars have seen extensive use in software test generation. A cov...
Grammars can serve as producers for structured test inputs that are syntactically correct by constru...
Grammar-based testing uses a given grammar to produce syntactically valid inputs. To cover program f...
When producing test inputs for a program, test generators ("fuzzers") can greatly profit from gramma...
Software testing is an important step in the software development life cycle. It focuses on testing ...
Grammars are traditionally used to recognize or parse sentences in a language, but they can also be ...
Automated generation of system level tests for grammar based systems requires the generation of comp...
peer reviewedGrammars can serve as producers for structured test inputs that are syntactically corre...
peer reviewedGrammars can serve as producers for structured test inputs that are syntactically corre...
Grammar-based test generation provides a systematic approach to producing test cases from a given co...
In this paper, we present an automatic grammar-based test generation approach which takes a symbolic...
In grammar-based testing, context-free grammars may be used to generate relevant test inputs for lan...
Grammar-based fuzzing is an effective method for testing programs that consume structured inputs, an...
In grammar-based testing, context-free grammars may be used to generate relevant test inputs for lan...
Part 2: Test Derivation MethodsInternational audienceIn grammar-based testing, context-free grammars...
Covering arrays and context-free grammars have seen extensive use in software test generation. A cov...
Grammars can serve as producers for structured test inputs that are syntactically correct by constru...
Grammar-based testing uses a given grammar to produce syntactically valid inputs. To cover program f...
When producing test inputs for a program, test generators ("fuzzers") can greatly profit from gramma...
Software testing is an important step in the software development life cycle. It focuses on testing ...
Grammars are traditionally used to recognize or parse sentences in a language, but they can also be ...
Automated generation of system level tests for grammar based systems requires the generation of comp...
peer reviewedGrammars can serve as producers for structured test inputs that are syntactically corre...
peer reviewedGrammars can serve as producers for structured test inputs that are syntactically corre...
Grammar-based test generation provides a systematic approach to producing test cases from a given co...
In this paper, we present an automatic grammar-based test generation approach which takes a symbolic...