2015
Ferrer, Javier; Kruse, Peter M; Chicano, Francisco; Alba, Enrique
Search based algorithms for test sequence generation in functional testing Journal Article
In: Information and Software Technology, 58 , pp. 419–432, 2015, ISSN: 09505849.
Abstract | Links | BibTeX | Tags: Ant Colony Optimization, Classification Tree Method, functional, Functional testing, Genetic Algorithm, search based software engineering, sequence, Test sequence generation
@article{Ferrer2014,
title = {Search based algorithms for test sequence generation in functional testing},
author = {Javier Ferrer and Peter M Kruse and Francisco Chicano and Enrique Alba},
url = {http://www.sciencedirect.com/science/article/pii/S0950584914001827},
doi = {10.1016/j.infsof.2014.07.014},
issn = {09505849},
year = {2015},
date = {2015-01-01},
journal = {Information and Software Technology},
volume = {58},
pages = {419--432},
abstract = {Context: The generation of dynamic test sequences from a formal specification, complementing traditional testing methods in order to find errors in the source code. Objective: In this paper we extend one specific combinatorial test approach, the Classification Tree Method (CTM), with transition information to generate test sequences. Although we use CTM, this extension is also possible for any combinatorial testing method. Method: The generation of minimal test sequences that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search-based approaches are required to find such (near) optimal test sequences. Results: The experimental analysis compares the search-based technique with a greedy algorithm on a set of 12 hierarchical concurrent models of programs extracted from the literature. Our proposed search-based approaches (GTSG and ACOts) are able to generate test sequences by finding the shortest valid path to achieve full class (state) and transition coverage. Conclusion: The extended classification tree is useful for generating of test sequences. Moreover, the experimental analysis reveals that our search-based approaches are better than the greedy deterministic approach, especially in the most complex instances. All presented algorithms are actually integrated into a professional tool for functional testing.},
keywords = {Ant Colony Optimization, Classification Tree Method, functional, Functional testing, Genetic Algorithm, search based software engineering, sequence, Test sequence generation},
pubstate = {published},
tppubtype = {article}
}
Context: The generation of dynamic test sequences from a formal specification, complementing traditional testing methods in order to find errors in the source code. Objective: In this paper we extend one specific combinatorial test approach, the Classification Tree Method (CTM), with transition information to generate test sequences. Although we use CTM, this extension is also possible for any combinatorial testing method. Method: The generation of minimal test sequences that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search-based approaches are required to find such (near) optimal test sequences. Results: The experimental analysis compares the search-based technique with a greedy algorithm on a set of 12 hierarchical concurrent models of programs extracted from the literature. Our proposed search-based approaches (GTSG and ACOts) are able to generate test sequences by finding the shortest valid path to achieve full class (state) and transition coverage. Conclusion: The extended classification tree is useful for generating of test sequences. Moreover, the experimental analysis reveals that our search-based approaches are better than the greedy deterministic approach, especially in the most complex instances. All presented algorithms are actually integrated into a professional tool for functional testing.
Dahi, Zakaria Abdelmoiz; Chaker, Mezioud; Draa, Amer
In: Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication, Association for Computing Machinery, Batna, Algeria, 2015, ISBN: 9781450334587.
Abstract | Links | BibTeX | Tags: Adaptation Strategies, Cellular Phone Networks, Error Correcting Code Problem, evolutionary algorithms, Genetic Algorithm, Transmission
@inproceedings{10.1145/2816839.2816843,
title = {Deterministically-Adaptive Genetic Algorithm To Solve Binary Communication Problems: Application On The Error Correcting Code Problem},
author = {Zakaria Abdelmoiz Dahi and Chaker, Mezioud and Draa, Amer},
url = {https://doi.org/10.1145/2816839.2816843},
doi = {10.1145/2816839.2816843},
isbn = {9781450334587},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication},
publisher = {Association for Computing Machinery},
address = {Batna, Algeria},
series = {IPAC '15},
abstract = {Global optimisation plays a critical role in today's scientific and industrial fields.
Optimisation problems are either continuous or combinatorial depending on the nature
of the parameters to optimise. In the class of combinatorial problems, we find a sub-category
which is the binary optimisation problems. Due to the complex nature of optimisation
problems, exhaustive search-based methods are no longer a good choice. So, metaheuristics
are more and more being opted in order to solve such problems. On the other hand,
most of the proposed metaheuristics were hand-tuned through a long and exhaustive
process that requires advanced knowledge. This fact makes them sensitive to any change
of the problem properties, that probably might decrease their efficiency. So, their
further application in real-life scenarios will be restricted or impossible. One of
the most active topic of research of nowdays is the adaptation strategies. These last
ones appear as a promising alternative to the hand-tuned approach. Deterministic adaptation
is one of the several adaptation schemes that exist. Based on the latter, in this
paper we propose several variants of one of the most studied metaheuristics; the Genetic
Algorithm (GA). The efficiency of the variants was assessed for solving a complex
optimisation problem in cellular networks which is the Error Correcting Code Problem
(ECCP). They were compared against a classical hand-tuned genetic algorithm. The experiments
gave promising results and encourage further investigation.},
keywords = {Adaptation Strategies, Cellular Phone Networks, Error Correcting Code Problem, evolutionary algorithms, Genetic Algorithm, Transmission},
pubstate = {published},
tppubtype = {inproceedings}
}
Global optimisation plays a critical role in today's scientific and industrial fields.
Optimisation problems are either continuous or combinatorial depending on the nature
of the parameters to optimise. In the class of combinatorial problems, we find a sub-category
which is the binary optimisation problems. Due to the complex nature of optimisation
problems, exhaustive search-based methods are no longer a good choice. So, metaheuristics
are more and more being opted in order to solve such problems. On the other hand,
most of the proposed metaheuristics were hand-tuned through a long and exhaustive
process that requires advanced knowledge. This fact makes them sensitive to any change
of the problem properties, that probably might decrease their efficiency. So, their
further application in real-life scenarios will be restricted or impossible. One of
the most active topic of research of nowdays is the adaptation strategies. These last
ones appear as a promising alternative to the hand-tuned approach. Deterministic adaptation
is one of the several adaptation schemes that exist. Based on the latter, in this
paper we propose several variants of one of the most studied metaheuristics; the Genetic
Algorithm (GA). The efficiency of the variants was assessed for solving a complex
optimisation problem in cellular networks which is the Error Correcting Code Problem
(ECCP). They were compared against a classical hand-tuned genetic algorithm. The experiments
gave promising results and encourage further investigation.
Optimisation problems are either continuous or combinatorial depending on the nature
of the parameters to optimise. In the class of combinatorial problems, we find a sub-category
which is the binary optimisation problems. Due to the complex nature of optimisation
problems, exhaustive search-based methods are no longer a good choice. So, metaheuristics
are more and more being opted in order to solve such problems. On the other hand,
most of the proposed metaheuristics were hand-tuned through a long and exhaustive
process that requires advanced knowledge. This fact makes them sensitive to any change
of the problem properties, that probably might decrease their efficiency. So, their
further application in real-life scenarios will be restricted or impossible. One of
the most active topic of research of nowdays is the adaptation strategies. These last
ones appear as a promising alternative to the hand-tuned approach. Deterministic adaptation
is one of the several adaptation schemes that exist. Based on the latter, in this
paper we propose several variants of one of the most studied metaheuristics; the Genetic
Algorithm (GA). The efficiency of the variants was assessed for solving a complex
optimisation problem in cellular networks which is the Error Correcting Code Problem
(ECCP). They were compared against a classical hand-tuned genetic algorithm. The experiments
gave promising results and encourage further investigation.