233,481 research outputs found

    Regression test selection for distributed Java RMI programs by means of formal concept analysis

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    Software maintenance is the process of modifying an existing system to ensure that it meets current and future requirements. As a result, performing regression testing becomes an essential but time consuming aspect of any maintenance activity. Regression testing is initiated after a programmer has made changes to a program that may have inadvertently introduced errors. It is a quality control approach to ensure that the newly modified code still complies with its specified requirements and that unmodified code has not been affected by the maintenance activity. In the literature various types of test selection techniques have been proposed to reduce the effort associated with re-executing the required test cases. However, the majority of these approach has been focusing only on sequential programs, and provide no or only very limited support for distributed programs or database-driven applications. The thesis presents a lightweight methodology, which applies Formal Concept Analysis to support a regression test selection analysis, in combination with execution trace collection and external data sharing analysis, for distributed Java RMI programs. Two Eclipse plug-ins were developed to automate the regression test selection process and to evaluate our methodology

    More than Dollars for Scholars: The Impact of the Dell Scholars Program on College Access, Persistence and Degree Attainment

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    Although college enrollment rates have increased substantially over the last several decades, socioeconomic inequalities in college completion have actually widened over time. A critical question, therefore, is how to support low-income and first-generation students to succeed in college after they matriculate. We investigate the impact of the Dell Scholars Program which provides a combination of generous financial support and individualized advising to scholarship recipients before and throughout their postsecondary enrollment. The program's design is motivated by a theory of action that, in order to meaningfully increase the share of lower-income students who earn a college degree, it is necessary both to address financial constraints students face and to provide ongoing support for the academic, cultural and other challenges that students experience during their college careers. We isolate the unique impact of the program on college completion by capitalizing on an arbitrary cutoff in the program's algorithmic selection process. Using a regression discontinuity design, we find that although being named a Dell Scholar has no impact on initial college enrollment or early college persistence, scholars at the margin of eligibility are significantly more likely to earn a bachelor's degree on-time or six years after high school graduation. These impacts are sizeable and represent a nearly 25 percent or greater increase in both four- and six-year bachelor's attainment. The program is resource intensive. Yet, back-of-theenvelope calculations indicate that the Dell Scholars Program has a positive rate of return

    Bounded Coordinate-Descent for Biological Sequence Classification in High Dimensional Predictor Space

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    We present a framework for discriminative sequence classification where the learner works directly in the high dimensional predictor space of all subsequences in the training set. This is possible by employing a new coordinate-descent algorithm coupled with bounding the magnitude of the gradient for selecting discriminative subsequences fast. We characterize the loss functions for which our generic learning algorithm can be applied and present concrete implementations for logistic regression (binomial log-likelihood loss) and support vector machines (squared hinge loss). Application of our algorithm to protein remote homology detection and remote fold recognition results in performance comparable to that of state-of-the-art methods (e.g., kernel support vector machines). Unlike state-of-the-art classifiers, the resulting classification models are simply lists of weighted discriminative subsequences and can thus be interpreted and related to the biological problem
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