Jonathan Corley

Using app inventor in a K-12 summer camp

Educators are often seeking new ways to motivate or inspire students to learn. Our past efforts in K-12 outreach included robotics and media computation as the contexts for teaching Computer Science (CS). With the deep interest in mobile technologies among teenagers, our recent outreach has focused on using smartphones as a new context. This paper is an experience report describing our approach and observations from teaching a summer camp for high school students using App Inventor (AI). The paper describes two separate methods (one using a visual block language, and another using Java) that were taught to high school students as a way to create Android applications. We observed that initiating the instruction with the block language, and then showing the direct mapping to an equivalent Java version, assisted students in understanding app development in Java. Our evaluation of the camp includes observations of student work and artifact assessment of student projects. Although the assessment suggests the camp was successful in several areas, we present numerous lessons learned based on our own reflection on the camp content and instruction.

Supporting efficient and advanced omniscient debugging for xDSMLs

Omniscient debugging is a promising technique that relies on execution traces to enable free traversal of the states reached by a system during an execution. While some General-Purpose Languages (GPLs) already have support for omniscient debugging, developing such a complex tool for any executable Domain-Specific Modeling Language (xDSML) remains a challenging and error prone task. A solution to this problem is to define a generic omniscient debugger for all xDSMLs. However, generically supporting any xDSML both compromises the efficiency and the usability of such an approach. Our contribution relies on a partly generic omniscient debugger supported by generated domain-specific trace management facilities. Being domain-specific, these facilities are tuned to the considered xDSML for better efficiency. Usability is strengthened by providing multidimensional omniscient debugging. Results show that our approach is on average 3.0 times more efficient in memory and 5.03 more efficient in time when compared to a generic solution that copies the model at each step.

Efficient and scalable omniscient debugging for model transformations

This paper discusses a technique for supporting omniscient debugging for model transformations, which are used to define core operations on software and system models. Similar to software systems developed using general-purpose languages, model transformations are also subject to human error and may possess defects. Existing model-driven engineering tools provide stepwise execution to aid developers in locating and removing defects. In this paper, we describe our investigation into a technique and associated algorithms that support omniscient debugging features for model transformations. Omniscient debugging enables enhanced navigation and exploration features during a debugging session beyond those possible in a strictly stepwise execution environment. Finally, the execution time performance is comparatively evaluated against stepwise execution, and the scalability (in terms of memory usage) is empirically investigated.

Evaluating the cloud architecture of AToMPM

In model-driven engineering, stakeholders work on models in order to design, transform, simulate, and analyze systems. Complex systems typically involve many stakeholder groups working in a coordinated manner on different aspects of a system. Therefore, there is a need for collaborative platforms to allow modelers to work together. Previously, we introduced the cloud-based multi-user tool AToMPM, designed to address the challenges for building a collaborative platform for modeling. This paper presents on the multi-user, multi-view architecture of AToMPM and an initial evaluation of its performance and scalability.

Searching for answers: an exploratory study of the formation, use, and impact of queries during debugging

This paper presents the results of an exploratory study investigating the formation, use, and impact of queries during debugging tasks. The results of this study provide additional evidence regarding the impact of query-based debuggers and can inform efforts that are focused on developing and improving query-based debuggers.