Browsing by Author "Tran, Ngoc M."
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Item Scaffolded Training Environment for Physics Programming (STEPP)(Association for Computing Machinery, Inc, 2019-06) Kitagawa, Midori; Fishwick, Paul Anthony; Kesden, Michael; Urquhart, Mary; Guadagno, R.; Jin, Rong; Tran, Ngoc M.; Omogbehin, Erik; Prakash, Aditya; Awaraddi, Priyanka; Hale, Baily; Suura, Ken; Raj, A.; Stanfield, J.; Vo, H.; Kitagawa, Midori; Fishwick, Paul Anthony; Kesden, Michael; Urquhart, Mary; Jin, Rong; Tran, Ngoc M.; Omogbehin, Erik; Prakash, Aditya; Awaraddi, Priyanka; Hale, Baily; Suura, KenWe are a year into the development of a software tool for modeling and simulation (M&S) of 1D and 2D kinematics consistent with Newton’s laws of motion. Our goal has been to introduce modeling and computational thinking into learning high-school physics. There are two main contributions from an M&S perspective: (1) the use of conceptual modeling, and (2) the application of Finite State Machines (FSMs) to model physical behavior. Both of these techniques have been used by the M&S community to model high-level “soft systems” and discrete events. However, they have not been used to teach physics and represent ways in which M&S can improve physics education. We introduce the NSF-sponsored STEPP project along with its hypothesis and goals. We also describe the development of the three STEPP modules, the server architecture, the assessment plan, and the expected outcomes. ©2019 Association of Computing Machinery.Item Statistical Learning of API Fully Qualified Names in Code Snippets of Online Forums(IEEE Computer Society) Phan, H.; Nguyen, H. A.; Tran, Ngoc M.; Truong, Linh H.; Nguyen, A. T.; Nguyen, Tien N.; Tran, Ngoc M.; Truong, Linh H.; Nguyen, Tien N.Software developers often make use of the online forums such as StackOverflow (SO) to learn how to use software libraries and their APIs. However, the code snippets in such a forum often contain undeclared, ambiguous, or largely unqualified external references. Such declaration ambiguity and external reference ambiguity present challenges for developers in learning to correctly use the APIs. In this paper, we propose StatType, a statistical approach to resolve the fully qualified names (FQNs) for the API elements in such code snippets. Unlike existing approaches that are based on heuristics, StatType has two well-integrated factors. We first learn from a large training code corpus the FQNs that often co-occur. Then, to derive the FQN for an API name in a code snippet, we use that knowledge and also leverage the context consisting of neighboring API names. To realize those factors, we treat the problem as statistical machine translation from source code with partially qualified names to source code with FQNs of the APIs. Our empirical evaluation on real-world code and StackOverflow posts shows that StatType achieves very high accuracy with 97.6% precision and 96.7% recall, which is 16.5% relatively higher than the state-of-the-art approach. © 2018 ACM.