Parsons Problems and Faded Parsons Problems have been shown to be effective in helping students in programming courses transition from passive learning, such as lectures or textbooks, to active learning in the form of writing code. We present FPPgen, an authoring system that largely automates the conversion of existing open-ended code-writing exercises to Faded Parsons Problems (FPPs). FPP solutions can be machine-checked using either spec-based autograding, in which student solutions are evaluated against instructor-provided test cases, or mutation-based autograding, in which students produce one or more unit tests that are evaluated using mutation testing. Our system allows creating exercises that rely on complex libraries and helper functions, such as student code intended to be run as part of a complex framework-based application. Our system also gracefully supports cumulative multi-part problems, in which later parts build on earlier parts. Python and Ruby exercises are currently supported, but FPPgen is language-agnostic and adding autograders for other languages is straightforward. In our experience so far, instructors can draft simple questions in less than an hour and mutation-based questions in about two hours as open-ended coding questions, and student helpers can use our tools to convert these to FPPs in less than an hour. FPPgen is in active use in both beginning and advanced large-enrollment programming courses in a CS undergraduate program at a large US university.

Software engineering is regularly listed as a top career, but typical curricula rarely equip college graduates to join latest industry practices. Providing a course on software deployment and operations (DevOps) gives students a place to learn how the practices of agile software engineering align with organizational culture and business objectives. Students can get hands-on experience with current industry practices, tools, and technologies along with broader context of their utility and purposes. This work invites discussion on the structure of a recently developed course on DevOps, including course modules, assignments, discussions, and projects. Reflections from a pilot offering are given, along with lessons learned and planned changes for the next offering.

Collaborative and competitive applications require that participants receive messages almost simultaneously and before a specified time. These requirements have been addressed by the delay variation-bounded multicasting tree (DVBMT) problem. In this paper, we propose the interval multicast subgraph (IMS) problem to address these requirements. IMS addresses these constraints with an interval of acceptable delay values for paths as user input from a source to a destination, eliminating the need to optimize delays for a variation value. By solving IMS rather than DVBMT and other variants of DVBMT, we are able to find solutions for larger graphs more efficiently. Our proposed interval multicast algorithm (IMA) accounts for an interval of acceptable delay as user input and guarantees the weight of each path from the source to a distinct destination is within the given interval if that path exists. We provide proofs of correctness and complexity of IMA, as well as simulation experiments, to illustrate the effects of various parameters on our algorithm. Simulations show that IMS is significantly less costly than finding the minimum variation for the average and best case. By remodeling the DVBMT problem to IMS, we have created a new problem that addresses the quality of service requirements of multicasting and is able to be solved efficiently for the average case for relatively large graphs.

Modeling the dynamics of biological processes has recently become an important research topic in computational biology and systems engineering. One of the most important reasons to model a biological process is to enable high-throughput in-silico experiments that attempt to predict or intervene in the process. These experiments can help accelerate the design of therapies through their rapid and inexpensive replication and alteration. While some techniques exist for reasoning with biological processes, few take advantage of the flexible and scalable algorithms popular in AI research. In reasoning about interventions in biological processes, where scalability is crucial for feasible application, we apply AI planning-based search techniques and demonstrate their advantage over existing enumerative methods. We also present a novel formulation of intervention planning that relies on models that characterize and attempt to change the phenotype of a system. We study three biological systems: the yeast cell cycle, a model of the human aging process, and the Wnt5a network governing the metastasis of melanoma in humans. The contribution of our investigation is in demonstrating that: (i) prior approaches, based on dynamic programming, cannot scale as well as heuristic search, and (ii) the newly found scalability enables us to plan previously unknown sequences of interventions that reveal novel and biologically significant responses in the systems which are consistent with biological knowledge in the literature.