Estimation of Test Code Changes Using Historical Release Data

In order to remain effective, test suites have to co-evolve alongside the production system. As such, quantifying the amount of changes in test code should be a part of effort estimation models for maintenance activities. In this paper, we verify to which extent (i) production code size, (ii) coverage measurements; and (iii) testability metrics predict the size of test code changes between two releases. For three Java and one C++ system, the size of production code changes appears to be the best predictor. We subsequently use this predictor to construct, calibrate and validate an estimation model using the historical release data. We demonstrate that is feasible to obtain a reliable prediction model, provided that at least 5 to 10 releases are available.