TY - GEN
T1 - Random unit-test generation with MUT-aware sequence recommendation
AU - Zheng, Wujie
AU - Zhang, Qirun
AU - Lyu, Michael
AU - Xie, Tao
PY - 2010
Y1 - 2010
N2 - A key component of automated object-oriented unit-test generation is to find method-call sequences that generate desired inputs of a method under test (MUT). Previous work cannot find desired sequences effectively due to the large search space of possible sequences. To address this issue, we present a MUT-aware sequence recommendation approach called RecGen to improve the effectiveness of random object-oriented unit-test generation. Unlike existing random testing approaches that select sequences without considering how a MUT may use inputs generated from sequences, RecGen analyzes object fields accessed by a MUT and recommends a short sequence that mutates these fields. In addition, for MUTs whose test generation keeps failing, RecGen recommends a set of sequences to cover all the methods that mutate object fields accessed by the MUT. This technique further improves the chance of generating desired inputs. We have implemented RecGen and evaluated it on three libraries. Evaluation results show that RecGen improves code coverage over previous random testing tools.
AB - A key component of automated object-oriented unit-test generation is to find method-call sequences that generate desired inputs of a method under test (MUT). Previous work cannot find desired sequences effectively due to the large search space of possible sequences. To address this issue, we present a MUT-aware sequence recommendation approach called RecGen to improve the effectiveness of random object-oriented unit-test generation. Unlike existing random testing approaches that select sequences without considering how a MUT may use inputs generated from sequences, RecGen analyzes object fields accessed by a MUT and recommends a short sequence that mutates these fields. In addition, for MUTs whose test generation keeps failing, RecGen recommends a set of sequences to cover all the methods that mutate object fields accessed by the MUT. This technique further improves the chance of generating desired inputs. We have implemented RecGen and evaluated it on three libraries. Evaluation results show that RecGen improves code coverage over previous random testing tools.
KW - Reliability
KW - Verification
UR - http://www.scopus.com/inward/record.url?scp=78649780853&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649780853&partnerID=8YFLogxK
U2 - 10.1145/1858996.1859054
DO - 10.1145/1858996.1859054
M3 - Conference contribution
AN - SCOPUS:78649780853
SN - 9781450301169
T3 - ASE'10 - Proceedings of the IEEE/ACM International Conference on Automated Software Engineering
SP - 293
EP - 296
BT - ASE'10 - Proceedings of the IEEE/ACM International Conference on Automated Software Engineering
T2 - 25th IEEE/ACM International Conference on Automated Software Engineering, ASE'10
Y2 - 20 September 2010 through 24 September 2010
ER -