A Random test generator generates executable tests together with their expected results. In the form of a noise-maker, it seeds the program with conditional scheduling primitives (such as yield()) that may cause conte...
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A Random test generator generates executable tests together with their expected results. In the form of a noise-maker, it seeds the program with conditional scheduling primitives (such as yield()) that may cause context switches. As a result different interleavings are potentially produced in different executions of the program. Determining a-priori the set of seeded locations required for a bug to manifest itself is rarely possible. This work proposes to reformulate random test generation of concurrent java programs as a search problem. Hence, it allows applying a set of well known search techniques from the domain of AI to the input space of the test generator. By iteratively refining the input parameters fed to the test generator, the search process creates testing scenarios (i.e. interleavings) that maximizes predefined objective functions. We develop geneticFinder, a noise-maker that uses a genetic algorithm as a search method. We demonstrate our approach by maximizing two objective functions: the high manifestation rate of concurrent bugs and the exporting of a high degree of debugging information to the user. Experimental results show our approach is effective.
Aalesund University College (AUC) has long and broad experience in the use of the java Concurrency Model for process control. An important part of this work is the design and control of small-scale embedded systems. T...
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ISBN:
(纸本)9780955301896
Aalesund University College (AUC) has long and broad experience in the use of the java Concurrency Model for process control. An important part of this work is the design and control of small-scale embedded systems. These systems include models of many kinds, such as vehicles, motion platforms, pendulums and simulated path tracking systems. The control strategies are mostly based on state space models, using modal and optimal control algorithms. Using realistic small-scale models, software and hardware capabilities and limitations can be tested for possible future full-scale industrial realizations.
Resource Description Framework (RDF) is commonly used for the semantic web query. During this decade, due to big data processing, the large numbers of RDF triples are crawled. The triples usually stored distributed on...
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ISBN:
(纸本)9781479908066;9781479908059
Resource Description Framework (RDF) is commonly used for the semantic web query. During this decade, due to big data processing, the large numbers of RDF triples are crawled. The triples usually stored distributed on the clouds storage or the large clusters. To search for the query answer, it is usually difficult to handle the search across platforms. Also, the search takes a long executed time. Thus, the data representation and platform are important to speedup the search and handle the heterogeneousness. In this paper, we present the experimental framework which can be used to handle the search of RDF data in GPU clusters. Our framework uses the java platform to manipulate the semantic query while using JCuda(1) to perform the GPU processing. Apache Cassandra storage, known as CumulusRDF, is used to store key-values for searching. In the experiments, DBpedia and Freebase dataset are extracted and manipulated. The triple structures are transformed and loaded into Apache Cassandra storage as CumulusRDF's flat layout. The subject-predicate-object keys are kept in CQL caching. There are about 3-hundred-million tags that can be handled with in one machine, which can reduce time, with an inexpensive cost. We shape the data grid from row-major-ordering of java, to GPU thread grid of CUDA, retrieved keys to join for finding the correspondence of the RDF graph.
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