Search Results - (( generating application testing algorithm ) OR ( java application stemming algorithm ))

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    An experimental study of neighbourhood based metaheuristic algorithms for test case generation satisfying the modified condition / decision coverage criterion by Haque, Ariful

    Published 2018
    “…The elaborated result of the study will help test engineers to choose the algorithm they need to generate test cases efficiently and optimally.…”
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    Thesis
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    Test suite generation based on hybrid flower pollination algorithm and hill climbing by Nasser, Abdullah B., Nor Wardah, Mohd Nasir, Kamal Zuhairi, Zamli, Waheeda Ali, H. M. Ghanem, Fakhrud, Din

    Published 2021
    “…The application of meta-heuristic algorithms in t-way tests generation, as an example of SBST, has as of late gotten to be predominant. …”
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    Conference or Workshop Item
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    Combinatorial test suites generation strategy utilizing the whale optimization algorithm by Ali Abdullah, Hassan, Salwani, Abdullah, Kamal Zuhairi, Zamli, Rozilawati, Razali

    Published 2020
    “…In the last 15 years, applications of meta-heuristics as the backbone of t-way test suite generation have shown promising results (e.g. …”
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    Article
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    Faculty timetabling using genetic algorithm by Liong, Boon Yaun

    Published 2011
    “…Faculty Timetabling using Genetic Algorithm (FTGA) is an application that generate optimum timetable for faculty.The target user of this application is faculty staff who responsible in generate timetable.The problem statement of the project is many clashing exist in the timetable.Faculty staff needs to solve the clashing manually.This will waste time and it is a problem for staff to solve the clashing.By implement GA,clashing will be reduced.The objective of the project is to develop aprototype in scheduling application for generates an optimum timetable for a faculty.Genetic algorithm will be implemented.The scope of FTGA is Faculty of Computer Systems & Software Engineering (FCSSE).The methodology use in this project is prototype model.The testing result show 95 out of 100 test cases achieved the maximum fitness value which means there is no clashing in the timetable.The maximum generation is set to 15 generation.Population for each generation is 3 populations.Percentage of FTGA solve the problem is 95%.…”
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    Undergraduates Project Papers
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    Artificial Bee Colony Algorithm for Pairwise Test Generation by Alazzawi, Ammar K., Homaid, Ameen A. Ba, Alomoush, Alaa A., Alsewari, Abdulrahman A.

    Published 2017
    “…In this paper, we evaluated and proposed a pairwise strategy named Pairwise Artificial Bee Colony algorithm (PABC). According to the benchmarking results, the PABC strategies outdo some existing strategies to generate a test case in many of the system configurations taken into consideration. …”
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    Article
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    Pairwise Test Data Generation based on Flower Pollination Algorithm by Nasser, Abdullah B., Alsewari, Abdulrahman A., Tairan, Nasser M., Kamal Z., Zamli

    Published 2017
    “…This paper proposes a new search-based strategy for generating the pairwise test suite, called Pairwise Flower Strategy (PairFS). …”
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    Article