Abstract

Test Case Prioritization Based on Artificial Immune Algorithm

Highlights

  • In order to better meet the needs of users, the update and iteration rate of intelligent terminal software systems has been accelerating

  • To address the problems of traditional genetic algorithm in solving the test case prioritization problem, this paper proposed a test case prioritization algorithm for intelligent terminal based on artificial immune algorithm

  • The experimental results showed that the algorithm based on the artificial immune algorithm was more capable of global search and less likely to fall into local optimum than the genetic algorithm, which indicated that the artificial immune algorithm was more stable and could better solve the test case prioritization problem

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Summary

INTRODUCTION

In order to better meet the needs of users, the update and iteration rate of intelligent terminal software systems has been accelerating. The intelligent terminal software systems have formed iterative development driven by regression testing [1]. By executing sequenced test cases, testers are able to find software defects earlier or at a lower cost [3]. It has been shown that test case prioritization is NP-hard problem [5], and traditional deterministic algorithms have difficulty in obtaining optimal solutions when dealing with the TCP problem. For this reason, swarm intelligence algorithms have been created and many optimization algorithms have been proposed [4, 6, 7]. The optimal test case sequence was found by this optimization algorithm, and the performance of that was analyzed by experimental results

RELATED WORK
Introduction to Artificial Immunity Algorithm
Coding Strategy Design
Affinity Function Design
Antibody Concentration Design
Antibody Excitation Degree Design
AIA-based Test Case Prioritization Optimization Algorithm Flow
Experimental Environment
Test Data Set
Experiment 2
CONCLUSION
Full Text
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