The category-partition method for specifying and generating fuctional tests
Communications of the ACM
Partition Testing Does Not Inspire Confidence (Program Testing)
IEEE Transactions on Software Engineering
Analyzing Partition Testing Strategies
IEEE Transactions on Software Engineering
The Z notation: a reference manual
The Z notation: a reference manual
Journal of Systems and Software - Special issue on applying specification, verification, and validation techniques to industrial software systems
On the Relationship Between Partition and Random Testing
IEEE Transactions on Software Engineering
Automatically Generating Test Data from a Boolean Specification
IEEE Transactions on Software Engineering
ZUM '95 Proceedings of the 9th International Conference of Z Usres on The Z Formal Specification Notation
Automating Test Case Generation from Z Specifications with Isabelle
ZUM '97 Proceedings of the 10th International Conference of Z Users on The Z Formal Specification Notation
Automating the Generation and Sequencing of Test Cases from Model-Based Specifications
FME '93 Proceedings of the First International Symposium of Formal Methods Europe on Industrial-Strength Formal Methods
A Mathematical Framework for the Investigation of Testing
IEEE Transactions on Software Engineering
Prioritization of Test Cases in MUMCUT Test Sets: An Empirical Study
Ada-Europe '02 Proceedings of the 7th Ada-Europe International Conference on Reliable Software Technologies
Test-Case Calculation through Abstraction
FME '01 Proceedings of the International Symposium of Formal Methods Europe on Formal Methods for Increasing Software Productivity
On the use of the classification-tree method by beginning software testers
Proceedings of the 2003 ACM symposium on Applied computing
On the Testing of Particular Input Conditions
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Improving evolutionary real-time testing
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Using formal specifications to support testing
ACM Computing Surveys (CSUR)
Proceedings of the 8th ACM SIGPLAN workshop on ERLANG
Using machine learning to refine Category-Partition test specifications and test suites
Information and Software Technology
Multi-paradigm Models as Source for Automated Test Construction
Electronic Notes in Theoretical Computer Science (ENTCS)
ZB'03 Proceedings of the 3rd international conference on Formal specification and development in Z and B
A safe regression testing approach for safety critical systems
Advances in Engineering Software
Choices, choices: comparing between CHOC'LATE and the classification-tree methodology
Ada-Europe'12 Proceedings of the 17th Ada-Europe international conference on Reliable Software Technologies
BETA: a b based testing approach
SBMF'12 Proceedings of the 15th Brazilian conference on Formal Methods: foundations and applications
Linking software testing results with a machine learning approach
Engineering Applications of Artificial Intelligence
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Software testing often consumes up to 50 percent of the overall software costs. A large amount of time and money within the test process is spent due to incomplete, inconsistent or ambiguos informal specifications of the test objects. A more formal approach to the early phases of software development can reduce the error rate drastically and, in addition, can significantly improve the central testing activities like test case design and test evaluation. This paper presents an approach for generating test cases from formal specifications written in Z by combining the classification-tree method for partition testing with the disjunctive normal form approach. Firstly, a classification tree describing high level test cases is constructed from the formal specification of the test object. Then the high level test cases are further refined by generating a disjunctive normal form for them. The refined test cases obtained this way cover all specified aspects of the system explicitly and also contain all information necessary to evaluate the test results. The proposed combination of the classification-tree method with the disjunctive normal form approach preserves advantages of both methods, overcomes most of their limitations, and can be supported by tools.