CMU SEI 2018 Research Review
• Collection
Publisher
Software Engineering Institute
Subjects
Abstract
This collection of presentations and posters and other material from our Research Review demonstrates our DoD-funded research in the following areas: risk reduction for rapid, affordable software development; creating operational resilience; making the recently possible mission practical; and automating test and evaluation. Among the presentations in the event were brief discussions called "lightning talks"; those short talks are included in this collection.
Collection Items
Modeling the Operations of the Vulnerability Ecosystem
• Poster
By Allen D. Householder
This poster describes models, metrics, datasets, and key performance indicators developed to improve vulnerability response.
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Predicting Security Flaws through Architectural Flaws
• Poster
By Robert Schiela, Rick Kazman
This poster describes efforts toward using automated architecture analysis to identify, prevent, and mitigate security flaws in code.
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Rapid Construction of Accurate Automatic Alert Handling
• Poster
By Lori Flynn
This poster describes the development of an extensible architecture for the classification and advanced prioritization of flaws in code.
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Rapid Software Composition by Assessing Untrusted Components
• Poster
By Rick Kazman
This poster describes efforts to increase the speed and confidence of the component selection process in software systems.
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Summarizing and Searching Video
• Poster
By Edwin J. Morris, Kevin A. Pitstick
This poster describes algorithms and a prototype developed to help analysts process information from video streams.
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Data-Driven Technical Debt Analysis
• Poster
By Ipek Ozkaya, Robert Nord
This poster describes research efforts in analyzing data to uncover technical debt.
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Timing Verification of Undocumented Multicore
• Poster
By Bjorn Andersson
This poster describes an abstraction and corresponding analysis that allow timing verification of undocumented hardware.
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Towards Security Defect Prediction with AI
• Poster
By Nathan M. VanHoudnos
This poster describes research comparing a state-of-the-art AI system to existing static analysis approaches for defect prediction.
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