Title
Test Oracle Generation Based on BPNN by Using the Values of Variables at Different Breakpoints for Programs
Abstract
Automatic test oracle generation is a bottleneck in realizing full automation of the entire software testing process. This study proposes a new method for automatically generating a test oracle for a new test input on the basis of several historical test cases by using a backpropagation neural network (BPNN) model. The new method is different from existing test oracle techniques. Specifically, our method has two steps. First, the values of variables are collected as training data when several historical test inputs are used to execute the program at different breakpoints. The test oracles (pass or fail) of these test cases are utilized to classify and label the training data. Second, a new test input is used to execute the program at different breakpoints, where the trained BPNN prediction model automatically generates its test oracle on the basis of the collected values of the variables involved. We conduct an experiment to validate our method. In the experiment, 113 faulty versions of seven types of programs are used as experimental objects. Results show that the average prediction accuracy rate of 74,651 test oracles is 95.8%. Although the failed test cases in the training data account for less than 5%, the overall average recall rate (prediction accuracy of test case execution failure) of all programs is 78.9%. Furthermore, the trained BPNN can reveal not only the impact of the values of variables but also the impact of the logical correspondence between variables in test oracle generation.
Year
DOI
Venue
2021
10.1142/S0218194021500492
INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
Keywords
DocType
Volume
Software testing, test oracle generation, variables and their values, BPNN
Journal
31
Issue
ISSN
Citations 
10
0218-1940
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Chunyan Ma151.08
Shaoying Liu271389.48
Jinglan Fu300.34
Tao Zhang421.77