دانلود مقاله ISI انگلیسی شماره 46203
عنوان فارسی مقاله

تطبیق بهینه سازی کلونی مورچه برای تولید داده های آزمون برای تست نرم افزار ساختاری

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
46203 2015 14 صفحه PDF سفارش دهید 11700 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Adapting ant colony optimization to generate test data for software structural testing ☆
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Swarm and Evolutionary Computation, Volume 20, February 2015, Pages 23–36

کلمات کلیدی
تولید اطلاعات آزمون - جست و جوی فرا ابتکاری - بهینه سازی کلونی مورچه - پوشش واحد - تابع تناسب اندام - ارزیابی تجربی
پیش نمایش مقاله
پیش نمایش مقاله تطبیق بهینه سازی کلونی مورچه برای تولید داده های آزمون برای تست نرم افزار ساختاری

چکیده انگلیسی

In general, software testing has been viewed as an effective way to improve software quality and reliability. However, the quality of test data has a significant impact on the fault-revealing ability of software testing activity. Recently, search-based test data generation has been treated as an operational approach to settle this difficulty. In the paper, the basic ACO algorithm is reformed into discrete version so as to generate test data for structural testing. First, the technical roadmap of combining the adapted ACO algorithm and test process together is introduced. In order to improve algorithm׳s searching ability and generate more diverse test inputs, some strategies such as local transfer, global transfer and pheromone update are defined and applied. The coverage for program elements is a special optimization objective, so the customized fitness function is constructed in our approach through comprehensively considering the nesting level and predicate type of branch. To validate the effectiveness of our ACO-based test data generation method, eight well-known programs are utilized to perform the comparative analysis. The experimental results show that our approach outperforms the existing simulated annealing and genetic algorithm in the quality of test data and stability, and is comparable to particle swarm optimization-based method. In addition, the sensitivity analysis on algorithm parameters is also employed to recommend the reasonable parameter settings for practical applications.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.