Paper Info
Title
Systolic Genetic Search for Software Engineering: The Test Suite Minimization Case.
Abstract
The Test Suite Minimization Problem (TSMP) is a NP-hard real-world problem that arises in the field of software engineering. It lies in selecting the minimal set of test cases from a large test suite, ensuring that the test cases selected cover a given set of elements of a computer program under test. In this paper, we propose a Systolic Genetic Search (SGS) algorithm for solving the TSMP. We use the global concept of SGS to derive a particular algorithm to explicitly exploit the high degree of parallelism available in modern GPU architectures. The experimental evaluation on seven real-world programs shows that SGS is highly effective for the TSMP, as it obtains the optimal solution in almost every single run for all the tested software. It also outperforms two competitive Genetic Algorithms. The GPU-based implementation of SGS has achieved a high performance, obtaining runtime reductions of up to 40x compared to its sequential implementation, and solving all the instances considered in less than nine seconds.
Year
DOI
Venue
2014
10.1007/978-3-662-45523-4_55
Lecture Notes in Computer Science
Keywords
Field
DocType
Systolic Genetic Search,Evolutionary Algorithms,Parallel Metaheuristics,GPU,GPGPU,Search-based Software Engineering
Test suite,Evolutionary algorithm,Software engineering,Degree of parallelism,Computer science,Parallel computing,Algorithm,Software,General-purpose computing on graphics processing units,Test case,Genetic algorithm,Search-based software engineering
Conference
Volume
ISSN
Citations 
8602
0302-9743
4
PageRank 
References 
Authors
0.45
10
3
Name
Order
Citations
PageRank
Martín Pedemonte1876.10
Francisco Luna214412.40
Enrique Alba33796242.34