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Theories in Practice: Easy-to-Write Specifications that Catch Bugs

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dc.contributor.advisor Michael Ernst en_US Saff, David en_US Boshernitsan, Marat en_US Ernst, Michael D. en_US
dc.contributor.other Program Analysis en_US 2008-01-15T14:15:58Z 2008-01-15T14:15:58Z 2008-01-14 en_US
dc.identifier.other MIT-CSAIL-TR-2008-002 en_US
dc.description.abstract Automated testing during development helps ensure that software works according to the test suite. Traditional test suites verify a few well-picked scenarios or example inputs. However, such example-based testing does not uncover errors in legal inputs that the test writer overlooked. We propose theory-based testing as an adjunct to example-based testing. A theory generalizes a (possibly infinite) set of example-based tests. A theory is an assertion that should be true for any data, and it can be exercised by human-chosen data or by automatic data generation. A theory is expressed in an ordinary programming language, it is easy for developers to use (often even easier than example-based testing), and it serves as a lightweight form of specification. Six case studies demonstrate the utility of theories that generalize existing tests to prevent bugs, clarify intentions, and reveal design problems. en_US
dc.format.extent 10 p. en_US
dc.relation Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory en_US
dc.subject JUnit, testing, partial specification en_US
dc.title Theories in Practice: Easy-to-Write Specifications that Catch Bugs en_US

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