I am a current fourth year student at the University of Virginia pursuing a major in computer science with a minor in mathematics. You can find my resume here. In the past, I have worked on research in software engineering with my advisor Dr. Mary Lou Soffa. This summer I worked in the field of Grid computing with Dr. Ann Chervenak, through CRA-W's Distributed Mentor Project, at the University of Southern California's Information Sciences Institute in Marina del Rey, California. I am currently planning to build off of this work for my senior thesis project.

I hope to pursue computer science research on a graduate level in the future. While I am still exploring various fields, my main interests lie in the broad areas of distributed and grid systems, high performance computing, and networks. I also enjoy certain aspects of computer architecture and theory.

Policy-Driven Data Management Many scientific collaborations make use of shared, distributed computational and storage resources to execute large scientific applications that process and generate petabytes of data, and to manage and distribute the volumes of data that these applications create. Data management is driven by many concerns, such as performance, reliability, security, and availability, among others. Data management goals and requirements can very from one scientific collaboration to another, and can be considered policies of the group of organizations and individuals that compose the collaboration. Data management policies, for example, might involve certain rules as to how the data is distributed among participating sites within the collaboration, or pertain to the degree of data replication that is maintained for the purposes of reliability and availability. Other policies might focus on which storage systems data can be placed on, or who is allowed to access the data, and how, in the interest of security. This research will seek to discover an appropriate method for implementing policy-driven data management in the context of current widespread Grid technologies, and compare its performance, in terms of scalability, availability, efficiency, and more, to alternative methods for incorporating policy concerns into data management schemes.

• Project website


• Sara Alspaugh, Ann Chervenak, and Ewa Deelman. Policy-Driven Data Management for Distributed Scientific Collaborations Using a Rule Engine. ACM Student Research Competition Best Undergraduate Student Poster in the proceedings of The International Conference for High Performance Computing, Networking, Storage and Analysis. Austin, Texas, November 2008. (summary: pdf) (poster: pdf)


• Sara Alspaugh and Ann Chervenak. Policy-Driven Data Management for Distributed Scientific Collaborations Using a Rule Engine. CRA-W Distributed Mentor Project Final Report. September 2008. (pdf)

Time-Aware Regression Test Suite Prioritization Regression test suite prioritization involves the reordering and selecting of the test cases of a regression test suite according to some metric. It is important in software development because for many applications, the regression test suite can be quite large, taking days or even weeks to execute, yet the time needed to fully execute all test cases is often not available. Thus, the goal is to be able to create a prioritization that efficiently executes in a time-constrained environment yet still effectively detects faults. As such, the key challenge that we addressed in this project was to determine whether it was possible to use 0/1 knapsack algorithms to create a prioritized test suite that executes in a small fraction of the time (e.g. 25%) that the original test suite takes to execute yet still covers a large fraction (e.g. 75%) of the code that the original test suite covers. In other words, to address this challenge, we compared several 0/1 knapsack algorithms to show how these could be used to identify a test suite reordering that rapidly covers the test requirements and always terminates within a specified testing time limit.

• Sara Alspaugh, Kristen R. Walcott, Michael Belanich, Gregory M. Kapfhammer, and Mary Lou Soffa. Efficient Time-Aware Prioritization with Knapsack Solvers. In the Proceedings of the ASE 2007 Workshop on Empirical Assessment of Software Engineering Languages and Technologies. Atlanta, Georgia, November 2007. (pdf) (ppt)
  

In my spare time, I like to see and do new things, read books about science and math, listen to NPR (especially On the Media, Wait, Wait...Don't Tell Me, and Science Friday), watch anime and documentaries, read underreported news stories, and occasionally explore places on foot. I have a cat. I'd like to visit the Pacific Northwest very soon. I look forward to a retirement during which I plan to sail the Caribbean like Buffett, explore Africa like Livingstone, and travel Europe like Hemmingway.

If you are looking for a good tax-deductible place to put your money please consider the Institute of Integrated Rural Development, an organization providing microfinancing, education, and support to the rural poor in Bangladesh. This organization hosted a trip to Bangladesh that I took with several other students in the spring of 2006, and having witnessed their work firsthand, I can vouch for all of the important and wonderful things they are doing for the people of this impoverished region.