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  1. Crossroads (CROS)
  2. Volume 14
  3. Volume 14, Issue 1, September 2007
  4. Adaptively ranking alerts generated from automated static analysis
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Volume 16
Volume 15
Volume 14
Volume 14, Issue 4, June 2008
Volume 14, Issue 3, March 2008
Volume 14, Issue 2, December 2007
Volume 14, Issue 1, September 2007
Introduction
Second life and education
Planning and improvisation in software processes
Trends in real-time rendering
An approach for detecting prosodic phrase boundaries in spoken english
The student's guide to GDC
Adaptively ranking alerts generated from automated static analysis
Storage capacity comparison of neural network models for memory recall
Observations on teamwork strategies in the ACM international collegiate programming contest
Volume 13
Volume 12
Volume 11
Volume 10
Volume 9
Volume 8
Volume 7
Volume 6
Volume 5
Volume 4
Volume 3
Volume 2
Volume 1

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Adaptively ranking alerts generated from automated static analysis

Content Provider ACM Digital Library
Author Heckman, Sarah Smith
Abstract Static analysis tools are useful for finding common programming mistakes that often lead to field failures. However, static analysis tools regularly generate a high number of false positive alerts, requiring manual inspection by the developer to determine if an alert is an indication of a fault. The adaptive ranking model presented in this paper utilizes feedback from developers about inspected alerts in order to rank the remaining alerts by the likelihood that an alert is an indication of a fault. Alerts are ranked based on the homogeneity of populations of generated alerts, historical developer feedback in the form of suppressing false positives and fixing true positive alerts, and historical, application-specific data about the alert ranking factors. The ordering of alerts generated by the adaptive ranking model is compared to a baseline of randomly-, optimally-, and static analysis tool-ordered alerts in a small role-based health care application. The adaptive ranking model provides developers with 81% of true positive alerts after investigating only 20% of the alerts whereas an average of 50 random orderings of the same alerts found only 22% of true positive alerts after investigating 20% of the generated alerts.
Starting Page 1
Ending Page 11
Page Count 11
File Format PDF HTM / HTML
ISSN 15284972 15284980
DOI 10.1145/1349332.1349339
Journal Crossroads (CROS)
Volume Number 14
Issue Number 1
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2003-03-01
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Content Type Text
Resource Type Article
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