407,512 research outputs found
Model Driven Mutation Applied to Adaptative Systems Testing
Dynamically Adaptive Systems modify their behav- ior and structure in
response to changes in their surrounding environment and according to an
adaptation logic. Critical sys- tems increasingly incorporate dynamic
adaptation capabilities; examples include disaster relief and space exploration
systems. In this paper, we focus on mutation testing of the adaptation logic.
We propose a fault model for adaptation logics that classifies faults into
environmental completeness and adaptation correct- ness. Since there are
several adaptation logic languages relying on the same underlying concepts, the
fault model is expressed independently from specific adaptation languages.
Taking benefit from model-driven engineering technology, we express these
common concepts in a metamodel and define the operational semantics of mutation
operators at this level. Mutation is applied on model elements and model
transformations are used to propagate these changes to a given adaptation
policy in the chosen formalism. Preliminary results on an adaptive web server
highlight the difficulty of killing mutants for adaptive systems, and thus the
difficulty of generating efficient tests.Comment: IEEE International Conference on Software Testing, Verification and
Validation, Mutation Analysis Workshop (Mutation 2011), Berlin : Allemagne
(2011
Repeatability of evolution on epistatic landscapes
Evolution is a dynamic process. The two classical forces of evolution are
mutation and selection. Assuming small mutation rates, evolution can be
predicted based solely on the fitness differences between phenotypes.
Predicting an evolutionary process under varying mutation rates as well as
varying fitness is still an open question. Experimental procedures, however, do
include these complexities along with fluctuating population sizes and
stochastic events such as extinctions. We investigate the mutational path
probabilities of systems having epistatic effects on both fitness and mutation
rates using a theoretical and computational framework. In contrast to previous
models, we do not limit ourselves to the typical strong selection, weak
mutation (SSWM)-regime or to fixed population sizes. Rather we allow epistatic
interactions to also affect mutation rates. This can lead to qualitatively
non-trivial dynamics. Pathways, that are negligible in the SSWM-regime, can
overcome fitness valleys and become accessible. This finding has the potential
to extend the traditional predictions based on the SSWM foundation and bring us
closer to what is observed in experimental systems
Dynamic fitness landscapes: Expansions for small mutation rates
We study the evolution of asexual microorganisms with small mutation rate in
fluctuating environments, and develop techniques that allow us to expand the
formal solution of the evolution equations to first order in the mutation rate.
Our method can be applied to both discrete time and continuous time systems.
While the behavior of continuous time systems is dominated by the average
fitness landscape for small mutation rates, in discrete time systems it is
instead the geometric mean fitness that determines the system's properties. In
both cases, we find that in situations in which the arithmetic (resp.
geometric) mean of the fitness landscape is degenerate, regions in which the
fitness fluctuates around the mean value present a selective advantage over
regions in which the fitness stays at the mean. This effect is caused by the
vanishing genetic diffusion at low mutation rates. In the absence of strong
diffusion, a population can stay close to a fluctuating peak when the peak's
height is below average, and take advantage of the peak when its height is
above average.Comment: 19 pages Latex, elsart style, 4 eps figure
LittleDarwin: a Feature-Rich and Extensible Mutation Testing Framework for Large and Complex Java Systems
Mutation testing is a well-studied method for increasing the quality of a
test suite. We designed LittleDarwin as a mutation testing framework able to
cope with large and complex Java software systems, while still being easily
extensible with new experimental components. LittleDarwin addresses two
existing problems in the domain of mutation testing: having a tool able to work
within an industrial setting, and yet, be open to extension for cutting edge
techniques provided by academia. LittleDarwin already offers higher-order
mutation, null type mutants, mutant sampling, manual mutation, and mutant
subsumption analysis. There is no tool today available with all these features
that is able to work with typical industrial software systems.Comment: Pre-proceedings of the 7th IPM International Conference on
Fundamentals of Software Engineerin
Mutation testing on an object-oriented framework: An experience report
This is the preprint version of the article - Copyright @ 2011 ElsevierContext
The increasing presence of Object-Oriented (OO) programs in industrial systems is progressively drawing the attention of mutation researchers toward this paradigm. However, while the number of research contributions in this topic is plentiful, the number of empirical results is still marginal and mostly provided by researchers rather than practitioners.
Objective
This article reports our experience using mutation testing to measure the effectiveness of an automated test data generator from a user perspective.
Method
In our study, we applied both traditional and class-level mutation operators to FaMa, an open source Java framework currently being used for research and commercial purposes. We also compared and contrasted our results with the data obtained from some motivating faults found in the literature and two real tools for the analysis of feature models, FaMa and SPLOT.
Results
Our results are summarized in a number of lessons learned supporting previous isolated results as well as new findings that hopefully will motivate further research in the field.
Conclusion
We conclude that mutation testing is an effective and affordable technique to measure the effectiveness of test mechanisms in OO systems. We found, however, several practical limitations in current tool support that should be addressed to facilitate the work of testers. We also missed specific techniques and tools to apply mutation testing at the system level.This work has been partially supported by the European Commission (FEDER) and Spanish Government under CICYT Project SETI (TIN2009-07366) and the Andalusian Government Projects ISABEL (TIC-2533) and THEOS (TIC-5906)
Bayesian co-estimation of selfing rate and locus-specific mutation rates for a partially selfing population
We present a Bayesian method for characterizing the mating system of
populations reproducing through a mixture of self-fertilization and random
outcrossing. Our method uses patterns of genetic variation across the genome as
a basis for inference about pure hermaphroditism, androdioecy, and gynodioecy.
We extend the standard coalescence model to accommodate these mating systems,
accounting explicitly for multilocus identity disequilibrium, inbreeding
depression, and variation in fertility among mating types. We incorporate the
Ewens Sampling Formula (ESF) under the infinite-alleles model of mutation to
obtain a novel expression for the likelihood of mating system parameters. Our
Markov chain Monte Carlo (MCMC) algorithm assigns locus-specific mutation
rates, drawn from a common mutation rate distribution that is itself estimated
from the data using a Dirichlet Process Prior (DPP) model. Among the parameters
jointly inferred are the population-wide rate of self-fertilization,
locus-specific mutation rates, and the number of generations since the most
recent outcrossing event for each sampled individual
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