کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
381015 1437457 2013 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Linking software testing results with a machine learning approach
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Linking software testing results with a machine learning approach
چکیده انگلیسی

Software testing techniques and criteria are considered complementary since they can reveal different kinds of faults and test distinct aspects of the program. The functional criteria, such as Category Partition, are difficult to be automated and are usually manually applied. Structural and fault-based criteria generally provide measures to evaluate test sets. The existing supporting tools produce a lot of information including: input and produced output, structural coverage, mutation score, faults revealed, etc. However, such information is not linked to functional aspects of the software. In this work, we present an approach based on machine learning techniques to link test results from the application of different testing techniques. The approach groups test data into similar functional clusters. After this, according to the tester's goals, it generates classifiers (rules) that have different uses, including selection and prioritization of test cases. The paper also presents results from experimental evaluations and illustrates such uses.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 26, Issues 5–6, May–June 2013, Pages 1631–1640
نویسندگان
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