The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform

Lionel C Briand - One of the best experts on this subject based on the ideXlab platform.

  • Empirical Investigation of Search Algorithms for Environment Model-based Testing of Real-time Embedded Software
    Proceedings of the 2012 International Symposium on Software Testing and Analysis (ISSTA '12), 2012
    Co-Authors: Muhammad Zohaib Iqbal, Andrea Arcuri, Lionel C Briand
    Abstract:

    System testing of real-time embedded systems (RTES) is a challenging task and only a fully automated testing approach can scale up to the testing requirements of industrial RTES. One such approach, which offers the advantage for testing teams to be black-box, is to use Environment Models to automatically generate test cases and oracles and an Environment simulator to enable earlier and more practical testing. In this paper, we propose novel heuristics for search-based, RTES system testing which are based on these Environment Models. We evaluate the fault detection effectiveness of two search-based algorithms, i.e., Genetic Algorithms and (1+1) Evolutionary Algorithm, when using these novel heuristics and their combinations. Preliminary experiments on 13 carefully selected, non-trivial artificial problems, show that, under certain conditions, these novel heuristics are effective at bringing the Environment into a state exhibiting a system fault. The heuristic combination that showed the best overall performance on the artificial problems was applied on an industrial case study where it showed consistent results. © 2012 ACM.

  • Combining search-based and adaptive random testing strategies for Environment Model-based testing of real-time embedded systems
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012
    Co-Authors: Muhammad Zohaib Iqbal, Andrea Arcuri, Lionel C Briand
    Abstract:

    Effective system testing of real-time embedded systems (RTES) re-quires a fully automated approach. One such black-box system testing approach is to use Environment Models to automatically generate test cases and test oracles along with an Environment simulator to enable early testing of RTES. In this paper, we propose a hybrid strategy, which combines (1+1) Evolutionary Algorithm (EA) and Adaptive Random Testing (ART), to improve the overall performance of system testing that is obtained when using each single strategy in isolation. An empirical study is carried out on a number of artificial problems and one industrial case study. The novel strategy shows significant overall im-provement in terms of fault detection compared to individual performances of both (1+1) EA and ART.

Ilya V. Kolmanovsky - One of the best experts on this subject based on the ideXlab platform.

  • Glider flight Environment Modeling for optimal control
    2012 American Control Conference (ACC), 2012
    Co-Authors: Dhaval D. Shah, Amor A. Menezes, Ilya V. Kolmanovsky
    Abstract:

    This paper describes the process of creating a computationally-inexpensive yet relatively accurate atmospheric Environment Model for use in stochastic optimal control problems for glider flight management. In such problems, estimates of transition probabilities between flight in updrafts, downdrafts and thermals of varying strength are needed. This work proposes an atmospheric Environment Model that predicts updraft and downdraft strengths in a given region and, when combined with existing glider flight data, estimates thermal locations and strengths. The resultant predictions can be utilized to compute the desired transition probabilities. A simple approach currently employed in flight simulator games is adapted for updraft and downdraft Modeling. The method is empirical and requires the computation of a linear factor. Interestingly, when validated against actual flight data, this technique is 92.4 percent accurate on average. This paper also shows that the location and intensity of thermals can be deduced from flight data by utilizing updraft and downdraft predictions. The work then illustrates the Modeling process for a sample topographical area, and utilizes the Model to solve a stochastic drift counteraction optimal control problem where control policies that maximize glider flight range are generated.

  • ACC - Glider flight Environment Modeling for optimal control
    2012 American Control Conference (ACC), 2012
    Co-Authors: Dhaval D. Shah, Amor A. Menezes, Ilya V. Kolmanovsky
    Abstract:

    This paper describes the process of creating a computationally-inexpensive yet relatively accurate atmospheric Environment Model for use in stochastic optimal control problems for glider flight management. In such problems, estimates of transition probabilities between flight in updrafts, downdrafts and thermals of varying strength are needed. This work proposes an atmospheric Environment Model that predicts updraft and downdraft strengths in a given region and, when combined with existing glider flight data, estimates thermal locations and strengths. The resultant predictions can be utilized to compute the desired transition probabilities. A simple approach currently employed in flight simulator games is adapted for updraft and downdraft Modeling. The method is empirical and requires the computation of a linear factor. Interestingly, when validated against actual flight data, this technique is 92.4 percent accurate on average. This paper also shows that the location and intensity of thermals can be deduced from flight data by utilizing updraft and downdraft predictions. The work then illustrates the Modeling process for a sample topographical area, and utilizes the Model to solve a stochastic drift counteraction optimal control problem where control policies that maximize glider flight range are generated.

Muhammad Zohaib Iqbal - One of the best experts on this subject based on the ideXlab platform.

  • Empirical Investigation of Search Algorithms for Environment Model-based Testing of Real-time Embedded Software
    Proceedings of the 2012 International Symposium on Software Testing and Analysis (ISSTA '12), 2012
    Co-Authors: Muhammad Zohaib Iqbal, Andrea Arcuri, Lionel C Briand
    Abstract:

    System testing of real-time embedded systems (RTES) is a challenging task and only a fully automated testing approach can scale up to the testing requirements of industrial RTES. One such approach, which offers the advantage for testing teams to be black-box, is to use Environment Models to automatically generate test cases and oracles and an Environment simulator to enable earlier and more practical testing. In this paper, we propose novel heuristics for search-based, RTES system testing which are based on these Environment Models. We evaluate the fault detection effectiveness of two search-based algorithms, i.e., Genetic Algorithms and (1+1) Evolutionary Algorithm, when using these novel heuristics and their combinations. Preliminary experiments on 13 carefully selected, non-trivial artificial problems, show that, under certain conditions, these novel heuristics are effective at bringing the Environment into a state exhibiting a system fault. The heuristic combination that showed the best overall performance on the artificial problems was applied on an industrial case study where it showed consistent results. © 2012 ACM.

  • Combining search-based and adaptive random testing strategies for Environment Model-based testing of real-time embedded systems
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012
    Co-Authors: Muhammad Zohaib Iqbal, Andrea Arcuri, Lionel C Briand
    Abstract:

    Effective system testing of real-time embedded systems (RTES) re-quires a fully automated approach. One such black-box system testing approach is to use Environment Models to automatically generate test cases and test oracles along with an Environment simulator to enable early testing of RTES. In this paper, we propose a hybrid strategy, which combines (1+1) Evolutionary Algorithm (EA) and Adaptive Random Testing (ART), to improve the overall performance of system testing that is obtained when using each single strategy in isolation. An empirical study is carried out on a number of artificial problems and one industrial case study. The novel strategy shows significant overall im-provement in terms of fault detection compared to individual performances of both (1+1) EA and ART.

Y U Guo - One of the best experts on this subject based on the ideXlab platform.

  • a Trusted Computing Environment Model in Cloud
    Machine Learning, 2010
    Co-Authors: Yong Shi, Y U Guo
    Abstract:

    The main difference between cloud computing and traditional enterprise internal IT services is that the owner and the user of cloud IT infrastructures are separated in cloud. This change requires a security duty separation in cloud computing. Cloud service providers (CSP) should secure the services they offer and cannot exceed the customers' authorities. Currently, no traditional information security products can meet this requirement. A multi-tenancy trusted computing Environment Model (MTCEM) is designed for IaaS delivery Model, and its purpose is to assure a trusted cloud infrastructure to customers. MTCEM presents a dual level transitive trust mechanism and supports a security duty separation function simultaneously. With MTCEM, CSP and customers can cooperate to build and maintain a trusted cloud computing Environment. MTCEM can be used to improve customers' confidence on cloud computing. The prototype of MTCEM shows that it has low impact on system performance and it is technically and practically feasible.

Eric J Miller - One of the best experts on this subject based on the ideXlab platform.

  • agent based housing market microsimulation for integrated land use transportation Environment Model system
    Elsevier, 2013
    Co-Authors: Adam Rosenfield, Franco Chingcuanco, Eric J Miller
    Abstract:

    The Housing Market Evolutionary System (HoMES) is the updated housing market module for the Integrated Land Use, Transportation, Environment (ILUTE) Model system. HoMES is a disaggregate, agent-based microsimulation of the owner, location choices and valuations, the endogenous supply of housing by type and location, and the endogenous determination of sale prices and rents. The new Model offers significant improvements over previous attempts by including a reformulated market clearing mechanism, market dependency on macro-economic conditions, and improved computational performance. A 100% synthesized population is validated against historical data for the Greater Toronto-Hamilton Area. © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [name organizer]

  • agent based housing market microsimulation for integrated land use transportation Environment Model system
    Procedia Computer Science, 2013
    Co-Authors: Adam Rosenfield, Franco Chingcuanco, Eric J Miller
    Abstract:

    Abstract The Housing Market Evolutionary System (HoMES) is the updated housing market module for the Integrated Land Use, Transportation, Environment (ILUTE) Model system. HoMES is a disaggregate, agent-based microsimulation of the owner-occupied housing market, with Models for households’ residential mobility decisions, location choices and valuations, the endogenous supply of housing by type and location, and the endogenous determination of sale prices and rents. The new Model offers significant improvements over previous attempts by including a reformulated market clearing mechanism, market dependency on macro-economic conditions, and improved computational performance. A 100% synthesized population is validated against historical data for the Greater Toronto-Hamilton Area.