Two students of computer science in the College of Engineering at Oregon State University received National Science Foundation (NSF) Graduate Research Fellowships that will provide three years of research funding while they attend graduate school. This prestigious award recognizes and supports outstanding early career graduate students in science, technology, engineering and mathematics disciplines.
Christopher Mendez, a graduate student, and Alannah Oleson, an undergraduate, received the awards for research in the field of human-computer interaction (HCI). There were a total of eight students across the U.S. to receive the award for HCI research.
This prestigious award recognizes and supports outstanding early career graduate students in science, technology, engineering and mathematics (STEM) disciplines. A total of 2,000 fellowships are awarded per year across all STEM fields.
Both Mendez and Oleson are advised by Distinguished Professor Margaret Burnett who co-founded the area of end-user software engineering, which aims to improve software for computer users who are not trained in programming. Her current research investigates gender-neutral software, uncovering gender inclusiveness issues in software from spreadsheets to programming environments.
Mendez and Oleson are extending Burnett’s research into different areas: Mendez is investigating how technology can empower people of low socioeconomic status; and Oleson is researching how best to teach inclusive software design methods and principles to university-level computer science students.
Mendez is continuing his research with Burnett at Oregon State, and Oleson will be starting graduate school next fall at the University of Washington.
The following quote comes from the the Education Committee of the Computing Research Association award announcement:
Margaret Burnett, Ph.D., is a distinguished professor in the School of Electrical and Computer Engineering at Oregon State University (OSU), a member of the ACM CHI Academy, and an ACM Distinguished Scientist. Burnett has contributed pioneering research on how ordinary users interact with software and optimizing that interaction. This resulted, in part, in the development of a new subarea, which is at the intersection of human-computer interaction and software engineering, called end-user software engineering.
Throughout her academic career, Burnett has continuously worked with undergraduate researchers and even accommodated high school students in her lab. She has mentored 39 undergraduate students in research; 21 were from underrepresented groups in computing, 32 co-authored published research papers, and 25 went on to graduate studies. A selection of the honors of her highly accomplished mentees includes three Google Scholarships, three NSF Graduate Fellowships, and two National Physical Sciences Consortium Graduate Fellowships. In her nomination, several mentees attested to her personal influence on and involvement in their lives and careers.
Impressively, Burnett influenced the culture of faculty undergraduate research mentoring in her school, increasing it to 50% participation. She has also led efforts to better support a diverse undergraduate population through trips to the Grace Hopper Celebration of Women in Computing, the adoption of a diversity plan, and new experimental scholarships for incoming freshmen women in computing. She has received awards from NCWIT, Microsoft, and OSU for her mentoring and research.
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.