Many computer science programs have capstone experiences or project courses that allow students to integrate knowledge from the full breadth of their major. Such capstone projects may be student-designed, instructor-designed, designed in conjunction with outside companies, or integrated with ongoing free and open source (FOSS) projects. The literature shows that the FOSS approach has attracted a great deal of interest, in particular when implemented with projects that have humanitarian goals (HFOSS). In this article, we describe five unique models from five distinct types of institutions for incorporating sustained FOSS or HFOSS (alternatively H/FOSS) project work into capstone experiences or courses. The goal is to provide instructors wishing to integrate open source experiences into their curriculum with additional perspectives and resources to help in adapting this approach to the specific needs and goals of their institution and students. All of the models presented are based on sustained engagement with H/FOSS projects that last at least one semester and often more. Each model is described in terms of its characteristics and how it fits the needs of the institution using the model. Assessment of each model is also presented. We then discuss the themes that are common across the models, such as project selection, team formation, mentoring, and student assessment. We examine the choices made by each model, as well as the challenges faced. We end with a discussion how the models have leveraged institutional initiatives and collaborations with outside organizations to address some of the challenges associated with these projects.

The rise of so-called "coding boot camps" as an alternative training ground for software development is prominent in the popular press, and these camps have caught the attention of colleges and universities. Administrators and faculty considering whether and how to partner with coding boot camps may want to consider what skills and knowledge boot camps are providing to students as well as successful college/boot camp partnerships. This poster reports on data from a collaborative NSF EHR grant (#1561705/ #1561717) funding a qualitative study of how coding boot camps and university CS programs prepare students for careers as software developers. As part of early data collection for this study, we have learned not only details of boot camp student preparation for the workforce, but also ways that universities are currently partnering with boot camps. This poster will report on data gathered in focus groups and interviews with curriculum developers from both coding boot camps and university CS programs categorized into the themes of: classifications of boot camps, screening/admission criteria, student profiles, training (both independently and in partnership with colleges/universities), and job placement. We draw suggestions from this initial data as to where boot camps may be able to enhance traditional CS degrees for students and what CS educators may want to consider when evaluating the boot camp experience.

We describe Data Science, a four-year undergraduate program in predictive analytics, machine learning, and data mining implemented at the College of Charleston, Charleston, South Carolina, USA. We present a ten-year status report detailing the program's origins, successes, and challenges. Our experience demonstrates that education and training for big data concepts are possible and practical at the undergraduate level. The development of this program parallels the growing demand for finding utility in data sets and streaming data. The curriculum is a seventy-seven credit-hour program that has been successfully implemented in a liberal arts and sciences institution by the faculties of computer science and mathematics.

The annual ACM International Collegiate Programming Contest produces a competitive paradigm that is at odds with the pedagogical goals of modern computer science and software engineering degree programs. This paradigm stresses the fast completion of a programming task and evaluates the results solely with black-box testing specified by the judges. In contrast, the pedagogical goals of contemporary college degree programs in computing emphasize the quality of processes inherent in software development and implementation. In 2007, the College of Charleston student chapter of the ACM hosted its annual high school programming competition by turning the conventional programming paradigm on its head to focus on quality-of-process rather than time-to-complete. The judging criteria included both technical and artistic merit. The implementation of the competition emphasized success by giving students working skeleton solution programs. This paper presents the motivation for the new paradigm, the details of its implementation for the 2007 competition, and the details of the new techniques for judging technical and artistic merit.

The presence of multiple faults in a program can inhibit the ability of fault-localization techniques to locate the faults. This problem occurs for two reasons: when a program fails, the number of faults is, in general, unknown; and certain faults may mask or obfuscate other faults. This paper presents our approach to solving this problem that leverages the well-known advantages of parallel work flows to reduce the time-to-release of a program. Our approach consists of a technique that enables more effective debugging in the presence of multiple faults and a methodology that enables multiple developers to simultaneously debug multiple faults. The paper also presents an empirical study that demonstrates that our parallel-debugging technique and methodology can yield a dramatic decrease in total debugging time compared to a one-fault-at-a-time, or conventionally sequential, approach.

A program's behavior is ultimately the collection of all its executions. This collection is diverse, unpredictable, and generally unbounded. Thus it is especially suited to statistical analysis and machine learning techniques. The primary focus of this paper is on the automatic classification of program behavior using execution data. Prior work on classifiers for software engineering adopts a classical batch-learning approach. In contrast, we explore an active-learning paradigm for behavior classification. In active learning, the classifier is trained incrementally on a series of labeled data elements. Secondly, we explore the thesis that certain features of program behavior are stochastic processes that exhibit the Markov property, and that the resultant Markov models of individual program executions can be automatically clustered into effective predictors of program behavior. We present a technique that models program executions as Markov models, and a clustering method for Markov models that aggregates multiple program executions into effective behavior classifiers. We evaluate an application of active learning to the efficient refinement of our classifiers by conducting three empirical studies that explore a scenario illustrating automated test plan augmentation.

Software products are often released with missing functionality or errors that result in failures in the field. In previous work, we presented the Gamma technology, which facilitates remote monitoring of deployed software and allows for a prompt reaction to failures. In this paper, we investigate one of the principal technologies on which Gamma is based: software tomography. Software tomography splits monitoring tasks across many instances of the software, so that partial information can be (1) collected from users by means of light-weight instrumentation and (2) merged to gather the overall monitoring information. After describing the technology, we illustrate an instance of software tomography for a specific monitoring task. We also present two case studies that we performed to evaluate the presented technique on a real program. The results of the studies show that software tomography can be successfully applied to collect accurate monitoring information using only minimal instrumentation on each deployed program instance.