Dynamic filtering and prioritization of static code analysis alerts

Title Dynamic filtering and prioritization of static code analysis alerts
Author Yüksel, U., Sözer, Hasan
Publication Date: 2021
Publication Place - IEEE
Subject Code reviews, Processing alarms/warnings/alerts, Program analysis, Prolog, Static code analysis
Type Document
Language English
Digital Yes
Manuscript No
Library: Özyeğin University
Library Asset ID 978-1-6654-2604-6
Record ID 71fa9abc-7c2a-4549-bbe2-2289ab50c500
Library Location Computer Science
Date 2021
Sample Text We propose an approach for filtering and prioritizing static code analysis alerts while these alerts are being reviewed by the developer. We construct a Prolog knowledge base that captures the data flow information in the source code as well as the reported alerts, their properties and associations with the data flow. The knowledge base is updated as the developer reviews the listed alerts and decides whether they point at an actual fault or not. These updates provide useful information since some of the alerts of the same type can be related in terms of their root cause. Hence, dynamically updated knowledge base can be queried to eliminate or prioritize the remaining alerts in the review list. We present a motivating example to illustrate the approach and its automation by integrating a set of tools.
DOI 10.1109/ISSREW53611.2021.00086
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Dynamic filtering and prioritization of static code analysis alerts

Author Yüksel, U., Sözer, Hasan
Publication Date 2021
Publication Place - IEEE
Subject Code reviews, Processing alarms/warnings/alerts, Program analysis, Prolog, Static code analysis
Type Document
Language English
Digital Yes
Manuscript No
Library Özyeğin University
Library Asset ID 978-1-6654-2604-6
Record ID 71fa9abc-7c2a-4549-bbe2-2289ab50c500
Library Location Computer Science
Date 2021
Sample Text We propose an approach for filtering and prioritizing static code analysis alerts while these alerts are being reviewed by the developer. We construct a Prolog knowledge base that captures the data flow information in the source code as well as the reported alerts, their properties and associations with the data flow. The knowledge base is updated as the developer reviews the listed alerts and decides whether they point at an actual fault or not. These updates provide useful information since some of the alerts of the same type can be related in terms of their root cause. Hence, dynamically updated knowledge base can be queried to eliminate or prioritize the remaining alerts in the review list. We present a motivating example to illustrate the approach and its automation by integrating a set of tools.
DOI 10.1109/ISSREW53611.2021.00086
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