The craft of software testing: subsystem testing including object-based and object-oriented testing
The craft of software testing: subsystem testing including object-based and object-oriented testing
Test program generation for functional verification of PowerPC processors in IBM
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
Code generation and analysis for the functional verification of micro processors
DAC '96 Proceedings of the 33rd annual Design Automation Conference
User defined coverage—a tool supported methodology for design verification
DAC '98 Proceedings of the 35th annual Design Automation Conference
Writing testbenches: functional verification of HDL models
Writing testbenches: functional verification of HDL models
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Coverage directed test generation for functional verification using bayesian networks
Proceedings of the 40th annual Design Automation Conference
Compacting regression-suites on-the-fly
APSEC '97 Proceedings of the Fourth Asia-Pacific Software Engineering and International Computer Science Conference
Multithreaded java program test generation
IBM Systems Journal
Smart diagnostics for configurable processor verification
Proceedings of the 42nd annual Design Automation Conference
A probabilistic alternative to regression suites
Theoretical Computer Science
Concurrent Java Test Generation as a Search Problem
Electronic Notes in Theoretical Computer Science (ENTCS)
Reaching coverage closure in post-silicon validation
HVC'10 Proceedings of the 6th international conference on Hardware and software: verification and testing
Proceedings of the 48th Design Automation Conference
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Random test generators are often used to create regression suites on-the-fly. Regression suites are commonly generated by choosing several specifications and generating a number of tests from each one, without reasoning which specification should be used and how many tests should be generated from each specification. This paper describes a technique for building high quality random regression suites. The proposed technique uses information about the probability of each test specification covering each coverage task. This probability is used, in turn, to determine which test specifications should be included in the regression suite and how many tests should be generated from each specification. Experimental results show that this practical technique can be used to improve the quality, and reduce the cost, of regression suites. Moreover, it enables better informed decisions regarding the size and distribution of the regression suites, and the risk involved.