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Improving random test sets using the diversity oriented test data generation
| Content Provider | ACM Digital Library |
|---|---|
| Author | Bueno, Paulo M. S. Wong, W. Eric Jino, Mario |
| Abstract | We present a measure that characterizes the diversity of a test set from the perspective of the input domain of the program under test. By using a metaheuristic algorithm, randomly generated test sets (RTS) are evolved towards Diversity Oriented Test Sets (DOTS), which thoroughly cover the input domain. DOTS are evaluated using a Monte Carlo simulation to assess how testing factors influence their effectiveness and also by the values of data flow coverage and mutation scores attained on simple programs. Results provide understanding on possible gains of using DOTS and on circumstances where RTS can be more effective. |
| Starting Page | 10 |
| Ending Page | 17 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781595938817 |
| DOI | 10.1145/1292414.1292419 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2007-11-06 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Random testing Software testing Diversity oriented test data generation Simulated repulsion Data flow testing Simulated annealing Test data generation Mutation testing Genetic algorithms |
| Content Type | Text |
| Resource Type | Article |
