In field studies of multiple mating and sperm competition there typically is no experimental control over the number of times that a female mates, the interval between matings, or the genetic identity of multiple fathers contributing to a brood. Irrespective of this complexity, high‐resolution molecular markers can be used to assign paternity with considerable confidence. This study employed two highly heterozygous microsatellite loci to assess multiple paternity and sperm displacement in a sample of broods taken from a natural population of Drosophila melanogaster. The large number of alleles present at each of the loci makes it difficult to derive explicit maximum‐likelihood estimates for multiple paternity and sperm displacement from brood samples. Monte Carlo simulations were used to estimate maximum‐likelihood parameters for the distribution of female remating frequency and the proportion of offspring sired by the second or subsequent mating males. Estimates were made based on genotypes scored at two distinct marker loci because they were found to give statistically homogeneous results. Fitting a Poisson distribution of number of matings, the mean number of males mated by a female was 1.82. The sperm displacement parameter estimated from doubly mated females were 0.79 and 0.86 for the two loci (0.83 for the joint estimate). The overall probability that a multiply mated female will be misclassified as singly mated was only 0.006, which indicates that microsatellites can provide excellent resolution for identifying multiple mating. In addition, microsatellites can be used to generate relatively precise estimates of sperm precedence in brood‐structured samples from a natural population.
Context: Agent-based models play an important role in simulating complex emergent phenomena and supporting critical decisions. In this context, a software fault may result in poorly informed decisions that lead to disastrous consequences. The ability to rigorously test these models is therefore essential.Objective: Our objective is to summarise the state-of-the-art techniques for test case generation in agent-based models and identify future research directions. Method:We have conducted a systematic literature review in which we pose five research questions related to the key aspects of test case generation in agent-based models: What are the information artifacts used to generate tests? How are these tests generated? How is a verdict assigned to a generated test? How is the adequacy of a generated test suite measured? What level of abstraction of an agent-based model is targeted by a generated test?Results: Out of the 464 initial search results, we identified 24 primary publications. Based on these primary publications, we formed a taxonomy to summarise the state-of-the-art techniques for test case generation in agentbased models. Our results show that whilst the majority of techniques are effective for testing functional requirements at the agent and integration levels of abstraction, there are comparatively few techniques capable of testing society-level behaviour. Furthermore, the majority of techniques cannot test non-functional requirements or "soft goals".Conclusions: This paper reports insights into the key developments and open challenges concerning test case generation in agent-based models that may be of interest to both researchers and practitioners. In particular, we identify the need for test case generation techniques that focus on societal and non-functional behaviour, and a more thorough evaluation using realistic case studies that feature challenging properties associated with a typical agent-based model.
This is a repository copy of Metamorphic testing with causal graphs.
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