Diagnosis-Oriented Alarm Correlations

Defect detection generally includes two stages: static analysis and alarm inspection. Helping the user in the alarm inspection task is a major challenge for current static analyzers. A large number of independent alarms are against the understanding and may lead developers and managers to reject the...

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Main Authors: Dalin Zhang, Dahai Jin, Yunzhan Gong, Hailong Zhang
Format: Conference Proceeding
Language:English
Subjects:
Abstract interpretation
Abstracts
Alarm correlations
Algorithm design and analysis
Approximation methods
Concrete
Correlation
Diagnosis
Inspection
Semantic slicing
Semantics
State slicing
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creator Dalin Zhang
Dahai Jin
Yunzhan Gong
Hailong Zhang
description Defect detection generally includes two stages: static analysis and alarm inspection. Helping the user in the alarm inspection task is a major challenge for current static analyzers. A large number of independent alarms are against the understanding and may lead developers and managers to reject the use of static analysis tools due to the overhead of alarm inspection. To help with the inspection tasks, we formally introduce alarm correlations. If the occurrence of one alarm causes another alarm to occur, we say they are correlated. We propose a framework for the investigation of the alarms, so as to help classifying them by their correlations. The underlying algorithms were implemented inside our static analysis tool. We choose one common semantic alarm as case study and proved that our method has the effect of reducing 33.1% of alarm identification. Using correlation information, we are able to automate alarm identification that previously had to be done manually.
doi_str_mv 10.1109/APSEC.2013.33
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subjects Abstract interpretation
Abstracts
Alarm correlations
Algorithm design and analysis
Approximation methods
Concrete
Correlation
Diagnosis
Inspection
Semantic slicing
Semantics
State slicing
title Diagnosis-Oriented Alarm Correlations
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