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Wyszukujesz frazę "Izadkhah, H." wg kryterium: Autor

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    Tytuł:
    Transforming Source Code to Mathematical Relations for Performance Evaluation
    Autorzy:
    Izadkhah, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/106274.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
    Tematy:
    Distributed Software Systems
    source code
    speed up
    Discrete Time Markov Chains
    Opis:
    Assessing software quality attributes (such as performance, reliability, and security) from source code is of the utmost importance. The performance of a software system can be improved by its parallel and distributed execution. The aim of the parallel and distributed execution is to speed up by providing the maximum possible concurrency in executing the distributed segments. It is a well known fact that distributing a program cannot be always caused speeding up the execution of it; in some cases, this distribution can have negative effects on the running time of the program. Therefore, before distributing a source code, it should be specified whether its distribution could cause maximum possible concurrency or not. The existing methods and tools cannot achieve this aim from the source code. In this paper, we propose a mathematical relationship for object oriented programs that statically analyze the program by verifying the type of synchronous and asynchronous calls inside the source code. Then, we model the invocations of the software methods by Discrete Time Markov Chains (DTMC). Using the properties of DTMC and the proposed mathematical relationship, we will determine whether or not the source code can be distributed on homogeneous processors. The experimental results showed that we can specify whether the program is distributable or not, before deploying it on the distributed systems.
    Źródło:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2015, 15, 2; 7-13
    1732-1360
    2083-3628
    Pojawia się w:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Software Systems Clustering Using Estimation of Distribution Approach
    Autorzy:
    Tajgardan, M.
    Izadkhah, H.
    Lotfi, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/108635.pdf
    Data publikacji:
    2016
    Wydawca:
    Społeczna Akademia Nauk w Łodzi
    Tematy:
    Software System
    Clustering, Genetic Algorithm
    Estimation of Distribution Algorithm (EDA)
    Probability Model
    Opis:
    Software clustering is usually used for program understanding. Since the software clustering is a NP-complete problem, a number of Genetic Algorithms (GAs) are proposed for solving this problem. In literature, there are two wellknown GAs for software clustering, namely, Bunch and DAGC, that use the genetic operators such as crossover and mutation to better search the solution space and generating better solutions during genetic algorithm evolutionary process. The major drawbacks of these operators are (1) the difficulty of defining operators, (2) the difficulty of determining the probability rate of these operators, and (3) do not guarantee to maintain building blocks. Estimation of Distribution (EDA) based approaches, by removing crossover and mutation operators and maintaining building blocks, can be used to solve the problems of genetic algorithms. This approach creates the probabilistic models from individuals to generate new population during evolutionary process, aiming to achieve more success in solving the problems. The aim of this paper is to recast EDA for software clustering problems, which can overcome the existing genetic operators’ limitations. For achieving this aim, we propose a new distribution probability function and a new EDA based algorithm for software clustering. To the best knowledge of the authors, EDA has not been investigated to solve the software clustering problem. The proposed EDA has been compared with two well-known genetic algorithms on twelve benchmarks. Experimental results show that the proposed approach provides more accurate results, improves the speed of convergence and provides better stability when compared against existing genetic algorithms such as Bunch and DAGC.
    Źródło:
    Journal of Applied Computer Science Methods; 2016, 8 No. 2; 99-113
    1689-9636
    Pojawia się w:
    Journal of Applied Computer Science Methods
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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