An auto-adapted method to generate pairwise test data set

Authors:
Penghui Fan;Shuyan Wang;Jiaze Sun
Affiliations:
School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China;School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China;School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China
Venue:
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Year:
2012

Citing 5
Cited 0

The AETG System: An Approach to Testing Based on Combinatorial Design

IEEE Transactions on Software Engineering
An Investigation of the Applicability of Design of Experiments to Software Testing

SEW '02 Proceedings of the 27th Annual NASA Goddard Software Engineering Workshop (SEW-27'02)
IPOG: A General Strategy for T-Way Software Testing

ECBS '07 Proceedings of the 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems
Generation of pairwise test sets using a simulated bee colony algorithm

IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Building Prioritized Pairwise Interaction Test Suites with Ant Colony Optimization

QSIC '09 Proceedings of the 2009 Ninth International Conference on Quality Software

Quantified Score

Hi-index	0.00

Visualization

Abstract

The pairwise test data set generation is one of key issues of combinatorial testing. This paper presents a novel auto-adapted method to generate a pairwise test data set. In this method, all test cases are made at a time, which is called "all-tests-at-a-time". Firstly, generate a certain number of test data sets; these test data sets have the same number of test cases. Secondly, chose the best data set and check whether it satisfy the requirements, if not ,go to next step, else the best is selected and the algorithm is end. Thirdly, update every data set: calculate the "repeat number" of each test case in a data set, chose two or three test cases according to the "repeat number"; update the selected test cases relies on "main factors" of each data set. Moreover, the classic examples are used to illustrate the performance of the proposed method. Compared with the existing algorithms, this paper provides an effective pairwise test suite generation method which updates test cases depend on the data set's coverage not any one independent case; it takes into the relationship of every test case consideration not like the traditional methods which also find the current best case. It can help the data set improve its coverage quickly.