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Content provided by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
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Automating Alert Handling Reduces Manual Effort

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Manage episode 310835910 series 3074403
Content provided by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Static analysis (SA) alerts about software code flaws require costly manual effort to validate (e.g., determine True or False) and repair. As a result, organizations often severely limit the types of alerts they manually examine to the types of code flaws they most worry about. That approach results in a tradeoff where many True flaws may never get fixed. To make alert handling more efficient, the SEI developed and tested novel software that enables the rapid deployment of a method to classify alerts automatically and accurately. We are implementing our solution in a new version of the SEI’s SCALe – the Source Code Analysis Lab – application.

  continue reading

102 episodes

Artwork
iconShare
 
Manage episode 310835910 series 3074403
Content provided by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Static analysis (SA) alerts about software code flaws require costly manual effort to validate (e.g., determine True or False) and repair. As a result, organizations often severely limit the types of alerts they manually examine to the types of code flaws they most worry about. That approach results in a tradeoff where many True flaws may never get fixed. To make alert handling more efficient, the SEI developed and tested novel software that enables the rapid deployment of a method to classify alerts automatically and accurately. We are implementing our solution in a new version of the SEI’s SCALe – the Source Code Analysis Lab – application.

  continue reading

102 episodes

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