Feedback-Directed Random Test Generation

Massachusetts Institute of Technology · Microsoft Research (United Kingdom) · +1 more institution

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Abstract

We present a technique that improves random test generation by incorporating feedback obtained from executing test inputs as they are created. Our technique builds inputs incrementally by randomly selecting a method call to apply and finding arguments from among previously-constructed inputs. As soon as an input is built, it is executed and checked against a set of contracts and filters. The result of the execution determines whether the input is redundant, illegal, contract-violating, or useful for generating more inputs. The technique outputs a test suite consisting of unit tests for the classes under test. Passing tests can be used to ensure that code contracts are preserved across program changes; failing…

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848
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FWCI
49.27
Percentile
100%
References
46
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Authors

4

Topics & keywords

Keywords
  • Computer science
  • Random testing
  • Test suite
  • Predicate abstraction
  • Algorithm
  • Unit testing
  • Code (set theory)
  • Test (biology)
UN Sustainable Development Goals
  • Peace, Justice and strong institutions
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