Title
PathART: path-sensitive adaptive random testing
Abstract
As test data widely spreading on the input domain may not thoroughly test the program's logic, in this paper, we propose an approach to generating test data widely spreading on a program's execution paths. In particular, we analyze execution paths of the program, distill constraints for executing the paths, calculate the path distance between test data according to their satisfaction for paths' constraints, and then generate test data far away from each other based on their path distance. The experimental results show that our approach significantly reduces the number of test data generated before the first fault is found.
Year
DOI
Venue
2013
10.1145/2532443.2532460
Internetware
Field
DocType
Volume
Behavioral pattern,Random testing,Computer science,Internet of Things,Real-time computing,Test data,Basis path testing,Test data generation
Conference
null
Issue
Citations 
PageRank 
null
0
0.34
References 
Authors
12
4
Name
Order
Citations
PageRank
Shanshan Hou12369.79
Chun Zhang270.82
Dan Hao386341.59
Lingming Zhang42726154.39