Winning iDash team
The winning iDASH team for the “Secure Outsourcing” challenge. Peter Rindal is second to the left.

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.

Margaret Burnet
Margaret Burnett gives a keynote address at FSE 2016.

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

Distinguished Paper Award
Distinguished Paper Award, FSE 2016. Pictured (left to right): Mihai Codoban (OSU alumus, now at Microsoft), Danny Dig (OSU), Michael Hilton (OSU) , Tien Nguyen (UT Dallas.) and three conference organizers.

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

Distinguished Paper Award, FSE 2016.
Distinguished Paper Award, FSE 2016. Pictured (left to right) Margaret Burnett (OSU), Scott Fleming (Univ. Memphis, former OSU postdoc), David Piorkowski (OSU alum, now at IBM Research), Austin Henley (Univ. Memphis), and three conference organizers.

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.