Future Aware Algorithms for Probabilistic Regression Suites

Automated regression suites are essential in developing large applications while maintaining reasonable quality and timetables. The main objection to automation of tests, in addition to the cost of creation and maintenance, is the observation that if you run the exact same test many times it becomes a lot less likely to find bugs. To alleviate those problems, a new regression suite practice, which uses random test generators to create regression suites on-the-fly, is becoming more common. In this regression practice, instead of maintaining tests, regression suites are generated on-the-fly by choosing a several specifications and generating a number of tests from each. This paper describes techniques for optimizing random generated regression suites. It first shows how the set cover greedy algorithms, commonly used for selecting tests for regression suites, may be adapted to selecting specifications for randomly generated regression suites. It then introduces a new class of greedy algorithms, namely future aware greedy. These algorithms are as computationally efficient and generate more effective regression suites.

By: Shady Copty, Shai Fine, Shmuel Ur, Avi Ziv

Published in: H-0251 in 2004


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