• DocumentCode
    685513
  • Title

    Diagnosis-Oriented Alarm Correlations

  • Author

    Dalin Zhang ; Dahai Jin ; Yunzhan Gong ; Hailong Zhang

  • Author_Institution
    State Key Lab. of Networking & Switching Technol., Beijing Univ. of Posts & Telecommun., Beijing, China
  • Volume
    1
  • fYear
    2013
  • fDate
    2-5 Dec. 2013
  • Firstpage
    172
  • Lastpage
    179
  • Abstract
    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.
  • Keywords
    inspection; program diagnostics; alarm identification automation; alarm identification reduction; alarm inspection; defect detection; diagnosis-oriented alarm correlations; semantic alarm; static analysis tools; static analyzers; Abstracts; Algorithm design and analysis; Approximation methods; Concrete; Correlation; Inspection; Semantics; Abstract interpretation; Alarm correlations; Diagnosis; Semantic slicing; State slicing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering Conference (APSEC), 2013 20th Asia-Pacific
  • Conference_Location
    Bangkok
  • ISSN
    1530-1362
  • Print_ISBN
    978-1-4799-2143-0
  • Type

    conf

  • DOI
    10.1109/APSEC.2013.33
  • Filename
    6805404