10,413 research outputs found
Genetic Algorithms for Redundancy in Interaction Testing
It is imperative for testing to determine if the components within
large-scale software systems operate functionally. Interaction testing involves
designing a suite of tests, which guarantees to detect a fault if one exists
among a small number of components interacting together. The cost of this
testing is typically modeled by the number of tests, and thus much effort has
been taken in reducing this number. Here, we incorporate redundancy into the
model, which allows for testing in non-deterministic environments. Existing
algorithms for constructing these test suites usually involve one "fast"
algorithm for generating most of the tests, and another "slower" algorithm to
"complete" the test suite. We employ a genetic algorithm that generalizes these
approaches that also incorporates redundancy by increasing the number of
algorithms chosen, which we call "stages." By increasing the number of stages,
we show that not only can the number of tests be reduced compared to existing
techniques, but the computational time in generating them is also greatly
reduced.Comment: Submitted to Genetic and Evolutionary Computation Conference 2020
(GECCO '20
Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation
Combinatorial interaction testing is an important software testing technique
that has seen lots of recent interest. It can reduce the number of test cases
needed by considering interactions between combinations of input parameters.
Empirical evidence shows that it effectively detects faults, in particular, for
highly configurable software systems. In real-world software testing, the input
variables may vary in how strongly they interact, variable strength
combinatorial interaction testing (VS-CIT) can exploit this for higher
effectiveness. The generation of variable strength test suites is a
non-deterministic polynomial-time (NP) hard computational problem
\cite{BestounKamalFuzzy2017}. Research has shown that stochastic
population-based algorithms such as particle swarm optimization (PSO) can be
efficient compared to alternatives for VS-CIT problems. Nevertheless, they
require detailed control for the exploitation and exploration trade-off to
avoid premature convergence (i.e. being trapped in local optima) as well as to
enhance the solution diversity. Here, we present a new variant of PSO based on
Mamdani fuzzy inference system
\cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive
selection of its global and local search operations. We detail the design of
this combined algorithm and evaluate it through experiments on multiple
synthetic and benchmark problems. We conclude that fuzzy adaptive selection of
global and local search operations is, at least, feasible as it performs only
second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the
best mean test suite size, the fuzzy adaptation even outperforms DPSO
occasionally. We discuss the reasons behind this performance and outline
relevant areas of future work.Comment: 21 page
Moving forward with combinatorial interaction testing
Combinatorial interaction testing (CIT) is an efficient and effective method of detecting failures that are caused by the interactions of various system input parameters. In this paper, we discuss CIT, point out some of the difficulties of applying it in practice, and highlight some recent advances that have improved CIT’s applicability to modern systems. We also provide a roadmap for future research and directions; one that we hope will lead to new CIT research and to higher quality testing of industrial systems
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