Automated classification of static code analysis alerts: a case study

Name: Automated classification of static code analysis alerts: a case study
Author: Yüksel, Ulaş, Sözer, Hasan

İsim	Automated classification of static code analysis alerts: a case study
Yazar	Yüksel, Ulaş, Sözer, Hasan
Basım Tarihi:	2013
Basım Yeri	- IEEE
Konu	Alert classification, Industrial case study, Static code analysis
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1063-6773
Kayıt Numarası	38548e7a-b4ed-433e-b0af-cc8779f5373b
Lokasyon	Computer Science
Tarih	2013
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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

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Automated classification of static code analysis alerts: a case study

Yazar Yüksel, Ulaş, Sözer, Hasan

Basım Tarihi 2013

Basım Yeri - IEEE

Konu Alert classification, Industrial case study, Static code analysis

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1063-6773

Kayıt Numarası 38548e7a-b4ed-433e-b0af-cc8779f5373b

Lokasyon Computer Science

Tarih 2013

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin 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