A Parallel Genetic Algorithm Based on Spark for Pairwise Test Suite Generation

Pairwise testing is an effective test generation technique that requires all pairs of parameter values to be by at least one test case. It has been proven that generating minimum test suite is an NP-complete problem covered Genetic algorithms have been used for pairwise test suite generation by rese...

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Bibliographic Details
Published inJournal of computer science and technology Vol. 31; no. 2; pp. 417 - 427
Main Authors Qi, Rong-Zhi, Wang, Zhi-Jian, Li, Shui-Yan
Format Journal Article
LanguageEnglish
Published New York Springer US 01.03.2016
Springer Nature B.V
College of Computer and Information, Hohai University, Nanjing 211106, China%College of Science, Hohai University, Nanjing 211106, China
Subjects
Algorithms
Analysis
Artificial Intelligence
Computation
Computer Science
Data Structures and Information Theory
Experiments
Fitness
Genetic algorithms
Genetics
Heuristic
Information Systems Applications (incl.Internet)
Mathematical models
NP-完全
Parallel processing
Platforms
Regular Paper
Software
Software Engineering
Studies
Theory of Computation
并行化
并行计算平台
并行遗传算法
测试套件
测试用例
火花
计算性能
test generation
Spark
pairwise testing
parallel genetic algorithm
Online AccessGet full text
ISSN1000-9000
1860-4749
DOI10.1007/s11390-016-1635-5

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Summary:Pairwise testing is an effective test generation technique that requires all pairs of parameter values to be by at least one test case. It has been proven that generating minimum test suite is an NP-complete problem covered Genetic algorithms have been used for pairwise test suite generation by researchers. However, it is always a time-consuming process, which leads to significant limitations and obstacles for practical use of genetic algorithms towards large-scale test problems. Parallelism will be an effective way to not only enhance the computation performance but also improve the quality of the solutions. In this paper, we use Spark, a fast and general parallel computing platform, to parallelize the genetic algorithm to tackle the problem. We propose a two-phase parallelization algorithm including fitness evaluation parallelization and genetic operation parallelization. Experimental results show that our algorithm outperforms the sequential genetic algorithm and competes with other approaches in both test suite size and computational performance. As a result, our algorithm is a promising improvement of the genetic algorithm for pairwise test suite generation.
Bibliography:11-2296/TP
Pairwise testing is an effective test generation technique that requires all pairs of parameter values to be by at least one test case. It has been proven that generating minimum test suite is an NP-complete problem covered Genetic algorithms have been used for pairwise test suite generation by researchers. However, it is always a time-consuming process, which leads to significant limitations and obstacles for practical use of genetic algorithms towards large-scale test problems. Parallelism will be an effective way to not only enhance the computation performance but also improve the quality of the solutions. In this paper, we use Spark, a fast and general parallel computing platform, to parallelize the genetic algorithm to tackle the problem. We propose a two-phase parallelization algorithm including fitness evaluation parallelization and genetic operation parallelization. Experimental results show that our algorithm outperforms the sequential genetic algorithm and competes with other approaches in both test suite size and computational performance. As a result, our algorithm is a promising improvement of the genetic algorithm for pairwise test suite generation.
pairwise testing, parallel genetic algorithm, Spark, test generation
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ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-016-1635-5