Three Oregon State University students working with the Jet Propulsion Laboratory received the Extreme Science and Engineering Discovery Environment (XSEDE) Startup Allocation based on their senior design capstone project.
Taylor Alexander Brown (computer science), Heidi Ann Clayton (computer science), and Xiaomei Wang (finance), also won the CH2M Multidisciplinary Collaboration Award at the 2017 Undergraduate Engineering Expo at Oregon State for their project called Coal and Open-pit surface mining impacts on American Lands (COAL).
The team created a system to process remote-sensing data to identify land surface types, coal mining operations, and the environmental impacts on water resources to help NASA’s Jet Propulsion Laboratory study the effects of coal mining on the environment.
The XSEDE award will allow the team to continue development on the project including the use of XSEDE resources for benchmarking, evaluation and experimentation. Funded by the National Science Foundation, XSEDE is a collection of integrated advanced digital resources and services.
“The availability and opportunity to use computational infrastructure of this caliber will further enable the development of a science gateway to continue foundational COAL research,” said Lewis John McGibbney, data scientist at the Jet Propulsion Laboratory, and the client for the project.
“I am extremely proud of the team’s achievements and know that such endeavors set a high standard for each and every one of them as they progress further through their journey in higher education and beyond.”
Graduate student Peter Rindal was on the winning team at an international computer security competition hosted by iDASH, a National Center for Biomedical Computing. The team members were interns and postdocs at Microsoft Research competing against seven other groups from around the world to win the “Secure Outsourcing” challenge.
“The competition pushed us to develop promising new research and brought us together with people in healthcare who want to see this technology in the real world,” Rindal said.
The goal of the competition was to advance the state-of-the-art for research on information privacy for genetic data. An application of their project could be secure cloud storage for medical data so patients and doctors could query data without revealing sensitive information to the cloud (e.g., predisposition to Alzheimer’s disease).
Specifically, the group calculated the probability of genetic diseases through matching a set of biomarkers to encrypted genomes stored in a commercial cloud service. The matching was carried out using a process called homomorphic encryption, which leaves no trace of the computation, so that only the patient and doctors can learn the answer to the question.
Oregon State University faculty and students were well represented at the premiere software engineering conference, ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2016) in Seattle November 13-18, 2016.
Distinguished Professor Margaret Burnett gave a keynote address titled Womenomics and Gender-Inclusive Software: What Software Engineers Need to Know, and five of the 74 papers presented there were from Oregon State which is an honor in itself. However, two of those papers were selected to receive Distinguished Paper Awards. Both papers aim to improve the efficiency of software development:
API Code Recommendation Using Statistical Learning from Fine-grained Changes
by Anh Nguyen, Michael Hilton, Mihai Codoban, Hoan Nguyen, Lily Mast, Eli Rademacher, Tien Nguyen and Danny Dig
Abstract: Learning and remembering how to use APIs is difficult. While code- completion tools can recommend API methods, browsing a long list of API method names and their documentation is tedious. Moreover, users can easily be overwhelmed with too much information. We present a novel API recommendation approach that taps into the predictive power of repetitive code changes to provide relevant API recommendations for developers. Our approach and tool, APIREC, is based on statistical learning from fine-grained code changes and from the context in which those changes were made. Our empirical evaluation shows that APIREC correctly recommends an API call in the first position 59% of the time, and it recommends the correct API call in the top 5 positions 77% of the time. This is a significant improvement over the state-of-the-art approaches by 30-160% for top-1 accuracy, and 10-30% for top-5 accuracy, respectively. Our result shows that APIREC performs well even with a one-time, minimal training dataset of 50 publicly available projects.
Foraging and Navigations, Fundamentally: Developers’ Predictions of Value and Cost
by David Piorkowski, Austin Henley, Tahmid Nabi, Scott Fleming, Christopher Scaffidi and Margaret Burnett
Abstract: Empirical studies have revealed that software developers spend 35%–50% of their time navigating through source code during development activities, yet fundamental questions remain: Are these percentages too high, or simply inherent in the nature of software development? Are there factors that somehow determine a lower bound on how effectively developers can navigate a given information space? Answering questions like these requires a theory that captures the core of developers’ navigation decisions. Therefore, we use the central proposition of Information Foraging Theory to investigate developers’ ability to predict the value and cost of their navigation decisions. Our results showed that over 50% of developers’ navigation choices produced less value than they had predicted and nearly 40% cost more than they had predicted. We used those results to guide a literature analysis, to investigate the extent to which these challenges are met by current research efforts, revealing a new area of inquiry with a rich and crosscutting set of research challenges and open problems.