نویسنده
Yüksel, Ulaş, Sözer, Hasan
تاریخ انتشار
2013
محل انتشار
-
IEEE
موضوع
Alert classification, Industrial case study, Static code analysis
نوع
belge
زبان
انگلیسی
دیجیتال
بله
نسخه خطی
خیر
کتابخانه
دانشگاه اوزیغین
شناسه دارایی کتابخانه
1063-6773
شماره ثبت
38548e7a-b4ed-433e-b0af-cc8779f5373b
محل کتابخانه
Computer Science
تاریخ
2013
یادداشتها
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
متن نمونه
Static code analysis tools automatically generate alerts for potential software faults that can lead to failures. However, developers are usually exposed to a large number of alerts. Moreover, some of these alerts are subject to false positives and there is a lack of resources to inspect all the alerts manually. To address this problem, numerous approaches have been proposed for automatically ranking or classifying the alerts based on their likelihood of reporting a critical fault. One of the promising approaches is the application of machine learning techniques to classify alerts based on a set of artifact characteristics. In this work, we evaluate this approach in the context of an industrial case study to classify the alerts generated for a digital TV software. First, we created a benchmark based on this code base by manually analyzing thousands of alerts. Then, we evaluated 34 machine learning algorithms using 10 different artifact characteristics and identified characteristics that have a significant impact. We obtained promising results with respect to the precision of classification.
DOI
10.1109/ICSM.2013.89
