Practical Selective Regression Testing with Effective Redundancy in Interleaved Tests

Abstract

As software systems evolve and change over time, test suites used for checking the correctness of software typically grow larger. Together with size, test suites tend to grow in redundancy. This is especially problematic for complex highly-configurable software domains, as growing the size of test suites significantly impacts the cost of regression testing. In this paper we present a practical approach for reducing ineffective redundancy of regression suites in continuous integration testing (strict constraints on time-efficiency) for highly-configurable software. The main idea of our approach consists in combining coverage based redundancy metrics (test overlap) with historical fault-detection effectiveness of integration tests, to identify ineffective redundancy that is eliminated from a regression test suite. We first apply and evaluate the approach in testing of industrial video conferencing software. We further evaluate the approach using a large set of artificial subjects, in terms of fault-detection effectiveness and timeliness of regression test feedback. We compare the results with an advanced retest-all approach and random test selection. The results show that regression test selection based on coverage and history analysis can: 1) reduce regression test feedback compared to industry practice (up to 39%), 2) reduce test feedback compared to the advanced retest-all approach (up to 45%) without significantly compromising fault-detection effectiveness (less than 0.5% on average), and 3) improve fault detection effectiveness compared to random selection (72% on average).