Selecting Software Test Data Using Data Flow Information
IEEE Transactions on Software Engineering
An Applicable Family of Data Flow Testing Criteria
IEEE Transactions on Software Engineering
Data flow coverage and the C language
TAV4 Proceedings of the symposium on Testing, analysis, and verification
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ICSE '97 Proceedings of the 19th international conference on Software engineering
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ICSE '94 Proceedings of the 16th international conference on Software engineering
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Journal of Systems and Software
An Experimental Comparison of the Effectiveness of Branch Testing and Data Flow Testing
IEEE Transactions on Software Engineering
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IEEE Transactions on Software Engineering
Cryptographic Verification of Test Coverage Claims
IEEE Transactions on Software Engineering
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IEEE Transactions on Software Engineering
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IEEE Transactions on Software Engineering
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SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
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Proceedings of the 27th international conference on Software engineering
Is mutation an appropriate tool for testing experiments?
Proceedings of the 27th international conference on Software engineering
Proceedings of the 27th international conference on Software engineering
A Characterisation Schema for Software Testing Techniques
Empirical Software Engineering
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Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Using Mutation Analysis for Assessing and Comparing Testing Coverage Criteria
IEEE Transactions on Software Engineering
An Empirical Study of Test Case Filtering Techniques Based on Exercising Information Flows
IEEE Transactions on Software Engineering
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Proceedings of the 30th international conference on Software engineering
Sufficient mutation operators for measuring test effectiveness
Proceedings of the 30th international conference on Software engineering
Data flow testing of service-oriented workflow applications
Proceedings of the 30th international conference on Software engineering
Analysis of test suite reduction with enhanced tie-breaking techniques
Information and Software Technology
The influence of size and coverage on test suite effectiveness
Proceedings of the eighteenth international symposium on Software testing and analysis
An Introduction to Software Testing
Electronic Notes in Theoretical Computer Science (ENTCS)
Component testing is not enough: a study of software faults in telecom middleware
TestCom'07/FATES'07 Proceedings of the 19th IFIP TC6/WG6.1 international conference, and 7th international conference on Testing of Software and Communicating Systems
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ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: technologies for mastering change - Volume Part I
Science of Computer Programming
Comparing non-adequate test suites using coverage criteria
Proceedings of the 2013 International Symposium on Software Testing and Analysis
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Journal of Systems and Software
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This paper reports on an empirical evaluation of the fault-detecting ability of two white-box software testing techniques: decision coverage (branch testing) and the all-uses data flow testing criterion. Each subject program was tested using a very large number of randomly generated test sets. For each test set, the extent to which it satisfied the given testing criterion was measured and it was determined whether or not the test set detected a program fault. These data were used to explore the relationship between the coverage achieved by test sets and the likelihood that they will detect a fault.Previous experiments of this nature have used relatively small subject programs and/or have used programs with seeded faults. In contrast, the subjects used here were eight versions of an antenna configuration program written for the European Space Agency, each consisting of over 10,000 lines of C code.For each of the subject programs studied, the likelihood of detecting a fault increased sharply as very high coverage levels were reached. Thus, this data supports the belief that these testing techniques can be more effective than random testing. However, the magnitudes of the increases were rather inconsistent and it was difficult to achieve high coverage levels.