A framework to advise tests using tests
Yurong Wang, Suzette Person, Sebastian G. Elbaum, Matthew B. Dwyer · 2014
Tests generated by different approaches can form a rich body of information about the system under test (SUT), which can then be used to amplify the power of test suites. Diversity in test representations, however, creates an obstacle to extracting and using this information. In this work, we introduce a test advice framework which enables extraction and application of information contained in existing tests to help improve other tests or test generation techniques. Our framework aims to 1) define a simple, yet expressive test case language so that different types of tests can be represented using a unified language, and 2) define an advice extraction function that enables the elicitation and application of the information encoded in a set of test cases. Preliminary results show how test advice can be used to generate amplified test suites with higher code coverage and improved mutants killed scores over the original test suite.