The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform
Saleh Al-sharaeh - One of the best experts on this subject based on the ideXlab platform.
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A multiple-population genetic algorithm for branch Coverage Test data generation
Software Quality Journal, 2011Co-Authors: Mohammad Alshraideh, Basel A. Mahafzah, Saleh Al-sharaehAbstract:The software Testing phase in the software development process is considered a time-consuming process. In order to reduce the overall development cost, automatic Test data generation techniques based on genetic algorithms have been widely applied. This research explores a new approach for using genetic algorithms as Test data generators to execute all the branches in a program. In the literature, existing approaches for Test data generation using genetic algorithms are mainly focused on maintaining a single-population of candidate Tests, where the computation of the fitness function for a particular target branch is based on the closeness of the input execution path to the control dependency condition of that branch. The new approach utilizes acyclic predicate paths of the program’s control flow graph containing the target branch as goals of separate search processes using distinct island populations. The advantages of the suggested approach is its ability to explore a greater variety of execution paths, and in certain conditions, increasing the search effectiveness. When applied to a collection of programs with a moderate number of branches, it has been shown experimentally that the proposed multiple-population algorithm outperforms the single-population algorithm significantly in terms of the number of executions, execution time, time improvement, and search effectiveness.
Mohammad Alshraideh - One of the best experts on this subject based on the ideXlab platform.
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A multiple-population genetic algorithm for branch Coverage Test data generation
Software Quality Journal, 2011Co-Authors: Mohammad Alshraideh, Basel A. Mahafzah, Saleh Al-sharaehAbstract:The software Testing phase in the software development process is considered a time-consuming process. In order to reduce the overall development cost, automatic Test data generation techniques based on genetic algorithms have been widely applied. This research explores a new approach for using genetic algorithms as Test data generators to execute all the branches in a program. In the literature, existing approaches for Test data generation using genetic algorithms are mainly focused on maintaining a single-population of candidate Tests, where the computation of the fitness function for a particular target branch is based on the closeness of the input execution path to the control dependency condition of that branch. The new approach utilizes acyclic predicate paths of the program’s control flow graph containing the target branch as goals of separate search processes using distinct island populations. The advantages of the suggested approach is its ability to explore a greater variety of execution paths, and in certain conditions, increasing the search effectiveness. When applied to a collection of programs with a moderate number of branches, it has been shown experimentally that the proposed multiple-population algorithm outperforms the single-population algorithm significantly in terms of the number of executions, execution time, time improvement, and search effectiveness.
Basel A. Mahafzah - One of the best experts on this subject based on the ideXlab platform.
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A multiple-population genetic algorithm for branch Coverage Test data generation
Software Quality Journal, 2011Co-Authors: Mohammad Alshraideh, Basel A. Mahafzah, Saleh Al-sharaehAbstract:The software Testing phase in the software development process is considered a time-consuming process. In order to reduce the overall development cost, automatic Test data generation techniques based on genetic algorithms have been widely applied. This research explores a new approach for using genetic algorithms as Test data generators to execute all the branches in a program. In the literature, existing approaches for Test data generation using genetic algorithms are mainly focused on maintaining a single-population of candidate Tests, where the computation of the fitness function for a particular target branch is based on the closeness of the input execution path to the control dependency condition of that branch. The new approach utilizes acyclic predicate paths of the program’s control flow graph containing the target branch as goals of separate search processes using distinct island populations. The advantages of the suggested approach is its ability to explore a greater variety of execution paths, and in certain conditions, increasing the search effectiveness. When applied to a collection of programs with a moderate number of branches, it has been shown experimentally that the proposed multiple-population algorithm outperforms the single-population algorithm significantly in terms of the number of executions, execution time, time improvement, and search effectiveness.
Cristian Cadar - One of the best experts on this subject based on the ideXlab platform.
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KLEE symbolic execution engine in 2019
International Journal on Software Tools for Technology Transfer, 2020Co-Authors: Cristian Cadar, Martin NowackAbstract:KLEE is a popular dynamic symbolic execution engine, initially designed at Stanford University and now primarily developed and maintained by the Software Reliability Group at Imperial College London. KLEE has a large community spanning both academia and industry, with over 60 contributors on GitHub, over 350 subscribers on its mailing list, and over 80 participants to a recent dedicated workshop. KLEE has been used and extended by groups from many universities and companies in a variety of different areas such as high-Coverage Test generation, automated debugging, exploit generation, wireless sensor networks, and online gaming, among many others.
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rwset attacking path explosion in constraint based Test generation
Tools and Algorithms for Construction and Analysis of Systems, 2008Co-Authors: Peter Boonstoppel, Cristian Cadar, Dawson EnglerAbstract:Recent work has used variations of symbolic execution to automatically generate high-Coverage Test inputs [3, 4, 7, 8, 14]. Such tools have demonstrated their ability to find very subtle errors. However, one challenge they all face is how to effectively handle the exponential number of paths in checked code. This paper presents a new technique for reducing the number of traversed code paths by discarding those that must have side-effects identical to some previously explored path. Our results on a mix of open source applications and device drivers show that this (sound) optimization reduces the numbers of paths traversed by several orders of magnitude, often achieving program Coverage far out of reach for a standard constraint-based execution system.
Dawson Engler - One of the best experts on this subject based on the ideXlab platform.
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rwset attacking path explosion in constraint based Test generation
Tools and Algorithms for Construction and Analysis of Systems, 2008Co-Authors: Peter Boonstoppel, Cristian Cadar, Dawson EnglerAbstract:Recent work has used variations of symbolic execution to automatically generate high-Coverage Test inputs [3, 4, 7, 8, 14]. Such tools have demonstrated their ability to find very subtle errors. However, one challenge they all face is how to effectively handle the exponential number of paths in checked code. This paper presents a new technique for reducing the number of traversed code paths by discarding those that must have side-effects identical to some previously explored path. Our results on a mix of open source applications and device drivers show that this (sound) optimization reduces the numbers of paths traversed by several orders of magnitude, often achieving program Coverage far out of reach for a standard constraint-based execution system.
