Software Engineering Institute | Carnegie Mellon University
Software Engineering Institute | Carnegie Mellon University

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Showing 1 - 5 of 5 results for the Author - sagar chaki , Publish Year - 2005

Technical Report | December 2005 - Technical Report Verification of Evolving Software via Component ... By Sagar Chaki, Edmund Clarke, Natasha Sharygina, Nishant Sinha

This 2005 report describes the application of the SEI Architecture Tradeoff Analysis Method (ATAM) to the U.S. Army's Warfighter Information Network-Tactical (WIN-T) system.

Technical Report | December 2005 - Technical Report Results of SEI Independent Research and Development ... By Rosann W. Collins, Rick Kazman, Richard C. Linger (Oak Ridge National Laboratory), Mark Pleszkoch, Stacy J. Prowell, Natasha Sharygina, Kurt C. Wallnau, Gwendolyn H. Walton, Charles B. Weinstock, Lutz Wrage, Sagar Chaki, Peter H. Feiler, John B. Goodenough, Aaron Greenhouse, Jörgen Hansson (University of Skovde), Alan R. Hevner (University of South Florida), John J. Hudak, Angel Jordan

This report describes the IR&D projects that were conducted during fiscal year 2005 (October 2004 through September 2005). In addition, this report provides information on what the SEI has learned in its role as a technology scout for developments over the past year in the field of software engineering.

Presentation | August 2005 - Presentation The ComFoRT Reasoning Framework By Sagar Chaki, James Ivers, Natasha Sharygina, Kurt C. Wallnau

Presented: August 2005

White Paper | July 2005 - White Paper The ComFoRT Reasoning Framework By Sagar Chaki, James Ivers, Natasha Sharygina, Kurt C. Wallnau

Model checking is a promising technology for verifying critical behavior of software. However, software model checking is hamstrung by scalability issues and is difficult for software engineers to use directly. ComFoRT addresses both of these challenges.

White Paper | December 2005 - White Paper Precise Buffer Overflow Detection via Model Checking By Sagar Chaki, Scott Hissam

In this paper, the authors present an automated overflow detection technique based on model checking and iterative refinement.