Automated GUI performance testing

A significant body of prior work has devised approaches for automating the functional testing of interactive applications. However, little work exists for automatically testing their performance. Performance testing imposes additional requirements upon GUI test automation tools: the tools have to be able to replay complex interactive sessions, and they have to avoid perturbing the application’s performance. We study the feasibility of using five Java GUI capture and replay tools for GUI performance test automation. Besides confirming the severity of the previously known GUI element identification problem, we also describe a related problem, the temporal synchronization problem, which is of increasing importance for GUI applications that use timer-driven activity. We find that most of the tools we study have severe limitations when used for recording and replaying realistic sessions of real-world Java applications and that all of them suffer from the temporal synchronization problem. However, we find that the most reliable tool, Pounder, causes only limited perturbation and thus can be used to automate performance testing. Based on an investigation of Pounder’s approach, we further improve its robustness and reduce its perturbation. Finally, we demonstrate in a set of case studies that the conclusions about perceptible performance drawn from manual tests still hold when using automated tests driven by Pounder. Besides the significance of our findings to GUI performance testing, the results are also relevant to capture and replay-based functional GUI test automation approaches.     

Adaptive Finite Element Simulation of the Time-dependent Simplified <Emphasis Type="Italic">P</Emphasis><Subscript><Emphasis Type="Italic">N</Emphasis></Subscript> Equations

The steady-state simplified P _N approximation to the radiative transfer equation has been successfully applied to many problems involving radiation. Recently, time-dependent simplified P _N equations have been derived by an asymptotic analysis similar to the asymptotic derivation of the steady-state SP _N equations (Frank et al. in J. Comput. Phys. 226:2289–2305, 2007). In this paper, we present computational results for the time-dependent SP _N equations in two dimensions, obtained by using an adaptive finite element approach. Several numerical comparisons with other existing models are shown.     

Statistical relational learning of trust

The learning of trust and distrust is a crucial aspect of social interaction among autonomous, mentally-opaque agents. In this work, we address the learning of trust based on past observations and context information. We argue that from the truster’s point of view trust is best expressed as one of several relations that exist between the agent to be trusted (trustee) and the state of the environment. Besides attributes expressing trustworthiness, additional relations might describe commitments made by the trustee with regard to the current situation, like: a seller offers a certain price for a specific product. We show how to implement and learn context-sensitive trust using statistical relational learning in form of a Dirichlet process mixture model called Infinite Hidden Relational Trust Model (IHRTM). The practicability and effectiveness of our approach is evaluated empirically on user-ratings gathered from eBay. Our results suggest that (i) the inherent clustering achieved in the algorithm allows the truster to characterize the structure of a trust-situation and provides meaningful trust assessments; (ii) utilizing the collaborative filtering effect associated with relational data does improve trust assessment performance; (iii) by learning faster and transferring knowledge more effectively we improve cold start performance and can cope better with dynamic behavior in open multiagent systems. The later is demonstrated with interactions recorded from a strategic two-player negotiation scenario.     

Distributed top-<Emphasis Type="Italic">k</Emphasis> aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network.     

Robust Cardiac Function Assessment in 4D PC‐MRI Data of the Aorta and Pulmonary Artery

Four‐dimensional phase‐contrast magnetic resonance imaging (4D PC‐MRI) allows the non‐invasive acquisition of time‐resolved, 3D blood flow information. Stroke volumes (SVs) and regurgitation fractions (RFs) are two of the main measures to assess the cardiac function and severity of valvular pathologies. The flow rates in forward and backward direction through a plane above the aortic or pulmonary valve are required for their quantification. Unfortunately, the calculations are highly sensitive towards the plane's angulation since orthogonally passing flow is considered. This often leads to physiologically implausible results. In this work, a robust quantification method is introduced to overcome this problem. Collaborating radiologists and cardiologists were carefully observed while estimating SVs and RFs in various healthy volunteer and patient 4D PC‐MRI data sets with conventional quantification methods, that is, using a single plane above the valve that is freely movable along the centerline. By default it is aligned perpendicular to the vessel's centerline, but free angulation (rotation) is possible. This facilitated the automation of their approach which, in turn, allows to derive statistical information about the plane angulation sensitivity. Moreover, the experts expect a continuous decrease of the blood flow volume along the vessel course. Conventional methods are often unable to produce this behaviour. Thus, we present a procedure to fit a monotonous function that ensures such physiologically plausible results. In addition, this technique was adapted for the usage in branching vessels such as the pulmonary artery. The performed informal evaluation shows the capability of our method to support diagnosis; a parameter evaluation confirms the robustness. Vortex flow was identified as one of the main causes for quantification uncertainties.     

An empirical analysis of the factors and measures of Enterprise Architecture Management success

Enterprise Architecture Management (EAM) is discussed in academia and industry as a vehicle to guide IT implementations, alignment, compliance assessment, or technology management. Still, a lack of knowledge prevails about how EAM can be successfully used, and how positive impact can be realized from EAM. To determine these factors, we identify EAM success factors and measures through literature reviews and exploratory interviews and propose a theoretical model that explains key factors and measures of EAM success. We test our model with data collected from a cross-sectional survey of 133 EAM practitioners. The results confirm the existence of an impact of four distinct EAM success factors, ‘EAM product quality’, ‘EAM infrastructure quality’, ‘EAM service delivery quality’, and ‘EAM organizational anchoring’, and two important EAM success measures, ‘intentions to use EAM’ and ‘Organizational and Project Benefits’ in a confirmatory analysis of the model. We found the construct ‘EAM organizational anchoring’ to be a core focal concept that mediated the effect of success factors such as ‘EAM infrastructure quality’ and ‘EAM service quality’ on the success measures. We also found that ‘EAM satisfaction’ was irrelevant to determining or measuring success. We discuss implications for theory and EAM practice.     

Local closed world reasoning with description logics under the well-founded semantics

An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases (Motik and Rosati, 2010 [52]), and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language.     

Continuous-time quantum Monte Carlo impurity solvers

Continuous-time quantum Monte Carlo impurity solvers are algorithms that sample the partition function of an impurity model using diagrammatic Monte Carlo techniques. The present paper describes codes that implement the interaction expansion algorithm originally developed by Rubtsov, Savkin, and Lichtenstein, as well as the hybridization expansion method developed by Werner, Millis, Troyer, et al. These impurity solvers are part of the ALPS-DMFT application package and are accompanied by an implementation of dynamical mean-field self-consistency equations for (single orbital single site) dynamical mean-field problems with arbitrary densities of states.

Program summary

Program title: dmftCatalogue identifier: AEIL_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEIL_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: ALPS LIBRARY LICENSE version 1.1No. of lines in distributed program, including test data, etc.: 899 806No. of bytes in distributed program, including test data, etc.: 32 153 916Distribution format: tar.gzProgramming language: C++Operating system: The ALPS libraries have been tested on the following platforms and compilers:

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  Linux with GNU Compiler Collection (g++ version 3.1 and higher), and Intel C++ Compiler (icc version 7.0 and higher)

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  MacOS X with GNU Compiler (g++ Apple-version 3.1, 3.3 and 4.0)

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  IBM AIX with Visual Age C++ (xlC version 6.0) and GNU (g++ version 3.1 and higher) compilers

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  Compaq Tru64 UNIX with Compq C++ Compiler (cxx)

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  SGI IRIX with MIPSpro C++ Compiler (CC)

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  HP-UX with HP C++ Compiler (aCC)

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  Windows with Cygwin or coLinux platforms and GNU Compiler Collection (g++ version 3.1 and higher)

RAM: 10 MB–1 GBClassification: 7.3External routines: ALPS [1], BLAS/LAPACK, HDF5Nature of problem: (See [2].) Quantum impurity models describe an atom or molecule embedded in a host material with which it can exchange electrons. They are basic to nanoscience as representations of quantum dots and molecular conductors and play an increasingly important role in the theory of “correlated electron” materials as auxiliary problems whose solution gives the “dynamical mean field” approximation to the self-energy and local correlation functions.Solution method: Quantum impurity models require a method of solution which provides access to both high and low energy scales and is effective for wide classes of physically realistic models. The continuous-time quantum Monte Carlo algorithms for which we present implementations here meet this challenge. Continuous-time quantum impurity methods are based on partition function expansions of quantum impurity models that are stochastically sampled to all orders using diagrammatic quantum Monte Carlo techniques. For a review of quantum impurity models and their applications and of continuous-time quantum Monte Carlo methods for impurity models we refer the reader to [2].Additional comments: Use of dmft requires citation of this paper. Use of any ALPS program requires citation of the ALPS [1] paper.Running time: 60 s–8 h per iteration.References:

• [1] 

  A. Albuquerque, F. Alet, P. Corboz, et al., J. Magn. Magn. Mater. 310 (2007) 1187.

• [2] 

  http://arxiv.org/abs/1012.4474, Rev. Mod. Phys., in press.

     

Multi-class supervised classification of electrical borehole wall images using texture features

Electrical borehole wall images represent micro-resistivity measurements at the borehole wall. The lithology reconstruction is often based on visual interpretation done by geologists. This analysis is very time-consuming and subjective. Different geologists may interpret the data differently. In this work, linear discriminant analysis (LDA) in combination with texture features is used for an automated lithology reconstruction of ODP (Ocean Drilling Program) borehole 1203A drilled during Leg 197. Six rock groups are identified by their textural properties in resistivity data obtained by a Formation MircoScanner (FMS). Although discriminant analysis can be used for multi-class classification, non-optimal decision criteria for certain groups could emerge. For this reason, we use a combination of 2-class (binary) classifiers to increase the overall classification accuracy. The generalization ability of the combined classifiers is evaluated and optimized on a testing dataset where a classification rate of more than 80% for each of the six rock groups is achieved. The combined, trained classifiers are then applied on the whole dataset obtaining a statistical reconstruction of the logged formation. Compared to a single multi-class classifier the combined binary classifiers show better classification results for certain rock groups and more stable results in larger intervals of equal rock type.     

Analytical derivation of traffic patterns in cache-coherent shared-memory systems

This paper presents an analytical method to derive the worst-case traffic pattern caused by a task graph mapped to a cache-coherent shared-memory system. Our analysis allows designers to rapidly evaluate the impact of different mappings of tasks to IP cores on the traffic pattern. The accuracy varies with the application’s data sharing pattern, and is around 65% in the average case and 1% in the best case when considering the traffic pattern as a whole. For individual connections, our method produces tight worst-case bandwidths.