Scalable Test Selection Using Source Code Deltas

As the number of automated regression tests increase, the ability to run all of them in a reasonable amount of time becomes more and more difficult, and simply doesn’t scale. Since we are looking for regressions, it is useful to hone in on the parts of the code that have changed from the last run to help select a small subset of tests that are likely to find the regression. In this way we are only running the tests that need to be run as your system gets larger and the number of possible tests scales outward. We have devised a method to select a subset of tests from an existing test set for scalable regression testing based on source code changes, or deltas. The selection algorithm is a static data mining technique that establishes the relationship between source code deltas and test case execution results. Test selection is then based on the established correlation. In this talk, we will discuss the benefits and also the pitfalls involved in having such an infrastructure. Finally, we will talk about how best to add it to a nightly or continuous test automation infrastructure.  Google Seattle Conference on Scalability,  Speaker: Ryan Gerard, Symantec Corporation

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