کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
433212 1441629 2016 24 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Model-based mutation testing—Approach and case studies
ترجمه فارسی عنوان
مدل سازی موتاسیون مبتنی بر آزمایش و روش مورد مطالعه؟
کلمات کلیدی
آزمایش موتاسیون، تست مبتنی بر مدل، تست جهش مبتنی بر مدل، اپراتور موتاسیون، قابلیت تشخیص خطا
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی

This paper rigorously introduces the concept of model-based mutation testing (MBMT) and positions it in the landscape of mutation testing. Two elementary mutation operators, insertion and omission, are exemplarily applied to a hierarchy of graph-based models of increasing expressive power including directed graphs, event sequence graphs, finite-state machines and statecharts. Test cases generated based on the mutated models (mutants) are used to determine not only whether each mutant can be killed but also whether there are any faults in the corresponding system under consideration (SUC) developed based on the original model. Novelties of our approach are: (1) evaluation of the fault detection capability (in terms of revealing faults in the SUC) of test sets generated based on the mutated models, and (2) superseding of the great variety of existing mutation operators by iterations and combinations of the two proposed elementary operators. Three case studies were conducted on industrial and commercial real-life systems to demonstrate the feasibility of using the proposed MBMT approach in detecting faults in SUC, and to analyze its characteristic features. Our experimental data suggest that test sets generated based on the mutated models created by insertion operators are more effective in revealing faults in SUC than those generated by omission operators. Worth noting is that test sets following the MBMT approach were able to detect faults in the systems that were tested by manufacturers and independent testing organizations before they were released.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Science of Computer Programming - Volume 120, 1 May 2016, Pages 25–48
نویسندگان
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