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Empirical Studies of a Prediction Model for Regression Test Selection (1998)
| Content Provider | CiteSeerX |
|---|---|
| Author | Rosenblum, David Rothermel, Gregg Harrold, Mary Jean Weyuker, Elaine |
| Abstract | Regression testing is an important testing 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) selects 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 running 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 version... |
| File Format | |
| Journal | IEEE Transactions On Software Engineering |
| Journal | IEEE TRANSACTIONS ON SOFTWARE ENGINEERING |
| Journal | IEEE Transactions on Software Engineering |
| Publisher Date | 1998-01-01 |
| Access Restriction | Open |
| Subject Keyword | Regression Testing Prediction Model Regression Test Selection Strategy Entire Test Suite Selective Regression Test Case Coverage-based Predictor Modified Software Large Proportion Regression Test Selection Empirical Study Important Testing Activity Test Suite Cost Model Software Maintenance |
| Content Type | Text |
