#### Empirical Studies of a Prediction Model for Regression Test Selection

M. J. Harrold, D. Rosenblum, G. Rothermel, and E. Weyuker

*IEEE Transactions on Software Engineering, *

V. 27, no. 3, March 2001, pp 248-263.

**Abstract**

Regression testing is an important
activity that can account for
a large proportion of the cost of
software maintenance.
One approach to reducing the cost of regression
testing is to employ a selective regression testing
technique that (1) chooses a subset of a test suite
that was used to test the software before the modifications, and
then (2) uses this subset to test the modified software.
Selective regression testing techniques reduce the cost of
regression testing if the cost of selecting the subset from the test suite
together with the cost of running the selected subset of test cases is less
than the cost of rerunning the entire test suite.

Rosenblum and Weyuker recently proposed
coverage-based predictors for use in predicting
the effectiveness of regression test selection strategies.
Using the regression testing cost model
of Leung and White, Rosenblum and Weyuker demonstrated the
applicability of these predictors with
respect to a case study involving 31 versions
of the KornShell.

To further investigate the applicability
of the Rosenblum-Weyuker (RW) predictor,
additional empirical studies have been performed.
The RW predictor was applied
to a number of subjects, using two different selective regression testing
tools, DejaVu and TestTube.
These studies support two conclusions.
First, they show that there is some variability in the success
with which the predictors work, and second, they suggest that
these results can be improved by incorporating information about
the distribution of modifications.
It is shown how the RW prediction model can
be improved to provide such an accounting.