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Filtering by: Alternative title ssim Report no. 1665-5 Remove constraint Alternative title ssim: Report no. 1665-5 Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Degree Level Doctoral Remove constraint Degree Level: Doctoral

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  • The Evolution of Software Entropy in Open Source projects: An Empirical Study

    Creator:
    Mannan, Umme Ayda
    Abstract:
    Software entropy impacts the overall quality of software systems. High entropy hinders developers from understanding the purpose of a piece of code and can cause developers to make sub-optimal changes and introduce bugs. Researchers have used entropy scores to measure the naturalness of code. However, thus far, no one has...
    Resource Type:
    Technical Report
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    documents or in some cases, design documents. To the best of our knowledge, no popular IDEs report on

  • Model Selection of Discrete Classifiers under Class and Cost Distribution Change: An Empirical Study

    Creator:
    Ahmed, Nahian and Hutchinson, Rebecca A.
    Abstract:
    A variety of important machine learning applications require predictions on test data with different characteristics than the data on which a model was trained and validated. In particular, test data may have a different relative frequency of positives and negatives (i.e., class distribution) and/or different mislabeling costs of false positive...
    Resource Type:
    Technical Report
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    model selection approaches. We report on this investigation in the appendix, but for now, there are no

  • A Wireless Energy Harvester for Powering Event-driven Internet-of-things Applications and Battery Charging

    Creator:
    Sadagopan, Kamala Raghavan
    Abstract:
    The need for sustainably powering unobtrusive internet-of-things applications has led to an interest in energy harvesting. Particularly, the proliferation of wireless communication and devices in the 2.4 GHz Industrial Scientific c Medical (ISM) band creates an opportunity to leverage commonly used devices for RF powering. This dissertation presents a low-quiescent-power...
    Resource Type:
    Dissertation
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    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Wireless Energy Harvesting . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1

  • Improving the Quality of Software Using Mutation Testing and Fault Prediction

    Creator:
    Ahmed, Iftekhar
    Abstract:
    Our confidence in software systems depends on our confidence in the exhaustiveness of our testing. As software systems get more complex, the task of exhaustive testing becomes more complex and even infeasible in some cases. In order to build less error prone systems, we therefore need to not only focus...
    Resource Type:
    Dissertation
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    me to his group and believed in me when no one else including me would believe in me. He helped me

  • Helping end-user programmers help themselves : the idea garden approach

    Creator:
    Cao, Chen
    Abstract:
    End-user programmers face many barriers in programming. Research has seen many programming environments that attempted to lower or remove the barriers but despite these efforts, empirical studies continue to report barriers users face. To investigate this issue, we took a theory-informed approach. Using theories from design, creativity, and problem solving...
    Resource Type:
    Dissertation
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    continue to report barriers users face. To investigate this issue, we took a theory-informed approach

  • Characterizing and Addressing Dataset Shift with Deep Learning

    Creator:
    Olson, Matthew L.
    Abstract:
    This dissertation delves into understanding, characterizing, and addressing dataset shift in deep learning, a pervasive issue for deployed machine learning systems. Integral aspects of the problem are examined: We start with the use of counterfactual explanations in order to characterize the behavior of deep reinforcement learning agents in visual input...
    Resource Type:
    Dissertation
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    , no matter how complicated. My mother for her excellent writing advice. My daughters for constantly

  • Evaluating the Impact of Live Programming on Collaborative Software Development

    Creator:
    Ghorashi, Seyed Soroush
    Abstract:
    Collaboration is tricky, but often beneficial in the context of numerous software related activities, from learning core concepts, to the design and implementation of large software products. The growth of online classes, from small structured seminars to massive open online courses (MOOCs), and the isolation and impoverished learning experience some...
    Resource Type:
    Dissertation
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    courses (MOOCs), and the isolation and impoverished learning experience some students report in these

  • Path-based dynamic impact analysis

    Creator:
    Law, James (James Bruce)
    Abstract:
    Successful software systems evolve over their lifetimes through the cumulative changes made by software maintainers. As software evolves, the problems resulting from software change worsen, exacerbated by increased system size and complexity, lack of program understanding, amount of effort required to make changes, and number of personnel involved. Experience shows...
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    Dissertation
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    approaches, present the first cost models for impact analysis techniques, and report the results of the

  • Information foraging in debugging

    Creator:
    Lawrance, Joseph A.
    Abstract:
    Programmers spend a substantial fraction of their debugging time by navigating through source code, yet little is known about how programmers navigate. With the continuing growth in size and complexity of software, this fraction of time is likely to increase, which presents challenges to those seeking both to understand and...
    Resource Type:
    Dissertation
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    theory to this domain. First, we conducted a lab study of 12 IBM programmers working on a bug report

  • Learning with Limited Labeled Data in Natural Language Processing

    Creator:
    Obeidat, Rasha
    Abstract:
    The advent of deep learning models leads to a substantial improvement in a wide range of NLP tasks, achieving state-of-art performances without any hand-crafted features. However, training deep models requires a massive amount of labeled data. Labeling new data as a new task or domain emerges consumes time and efforts...
    Resource Type:
    Dissertation
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    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5 Conclusion 66 Bibliography 69 LIST OF FIGURES Figure Page 1.1 Traditional Supervised