Search Constraints

Start Over
Filtering by: Creator Fitzpatrick, Martin S. Remove constraint Creator: Fitzpatrick, Martin S. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

Search Results

  • Variational Typing and Its Applications

    Creator:
    Chen, Sheng
    Abstract:
    The study of variational typing originated from the problem of type inference for variational programs, which encode numerous different but related plain programs. In this dissertation, I present a sound and complete type inference algorithm for inferring types of all plain programs encoded in variational programs. The proposed algorithm runs...
    Resource Type:
    Dissertation
    Full Text:
    presented on July 1, 2015. Title: Variational Typing and Its Applications Abstract approved: Martin

  • Update programming -- a safe approach to software maintenance

    Creator:
    Ren, Deling
    Abstract:
    Software maintenance accounts for a large portion of the software development cost, particularly the process of updating programs either to adapt for requirement change or to enhance design or efficiency. Currently, program updates are generally performed manually by programmers using text editors. This is an unreliable method because syntax and...
    Resource Type:
    Dissertation
    Full Text:
    ˜ � “�“�›/œ‚–X¦Z§¨—žœ � œb¥t—�©y–b›/˜Œª«–X¡¢‚—w˜P¢�–b¢�¦i˜ oHG #%$'&)()*,+�-.$/* Q¬#%…SO;O,sa-�]vQC]^…S­

  • Using Change Representations to Analyze Properties of Variational Software

    Creator:
    Wang, Xiangyu
    Abstract:
    Appropriate representations of variational software simplify the analysis of their properties.This thesis proposes tailored representations of two kinds variational softwares: difference files of merge commits in Git and feature models. For the former, we use the Choice Edit Model, which is based on the choice calculus, to represent changes introduced...
    Resource Type:
    Masters Thesis
    Full Text:
    Analyze Properties of Variational Software Abstract approved: Martin Erwig Appropriate representations

  • Galileo - a system for analyzing Java bytecode database handlers subsystem

    Creator:
    Balasubramanian, Eswar, Sharma, Hitesh, and Thorat, Aarti
    Abstract:
    Analysis of programs forms an important activity in the field of software engineering. It is necessary to help understand the code, which facilitates comprehensive testing, maintenance and optimization of code. Aristotle is a tool for analyzing programs written in C. We have designed a system on similar lines for Java...
    Resource Type:
    Capstone Project
    Full Text:
    requirements. User Analysis Programs ,:<: · xtcnd s>> Figure 4.1 - Use Case Diagram for Galileo Use

  • The Ownership Monad

    Creator:
    McGirr, Michael G.
    Abstract:
    The Rust programming language is a systems programming language with a strong static type system. A central feature of Rust’s type system is its unique concept of “ownership”, which enables Rust to give a user safe, low-level control over resources without the overhead of garbage collection. In Haskell, most data...
    Resource Type:
    Capstone Project
    Full Text:
    computation. 26 continueOwn :: Own a -> Own (Either String a) continueOwn x = do s <- get liftIO

  • Variational Satisfiability Solving

    Creator:
    Young, Jeffrey M.
    Abstract:
    Over the last two decades, satisfiability and satisfiability-modulo theory (SAT/SMT) solvers have grown powerful enough to be general purpose reasoning engines throughout software engineering and computer science. However, most practical use cases of SAT/SMT solvers require not just solving a single SAT/SMT problem, but solving sets of related SAT/SMT problems....
    Resource Type:
    Dissertation
    Full Text:
    : Martin Erwig and Prasad Tade- palli. Thank you Martin for your insight, sharp wit, and clever

  • A General Purpose Agent-Based Blackboard Optimization Method Applied to Sodium Fast Reactor Design and Analysis

    Creator:
    Stewart, Ryan H.
    Abstract:
    We present a novel multi-objective optimization methodology built upon a multi-agent blackboard framework. This multi-agent blackboard system (MABS) synthesizes blackboard architectures, multi-agent environments, and optimization theory. The blackboard architecture creates the framework for initializing, storing, and solving a multi-objective optimization problem. Multiple agents allow for an optimization problem to be...
    Resource Type:
    Dissertation
    Full Text:
    Abstract approved: Todd S. Palmer We present a novel multi-objective optimization methodology built upon

  • Pedestrian distraction : pedestrian behaviors at midblock crossings considering geometric and environmental conditions

    Creator:
    Kautz, Julia Anne
    Abstract:
    Pedestrian distraction at roadway crossings has been correlated with a higher risk of pedestrian-vehicle collisions due to the pedestrian's cognitive, visual, and motor attention being drawn to a wide variety of secondary tasks. This study is different from previous field studies of pedestrian midblock crossings in that the geometric layout...
    Resource Type:
    Masters Thesis
    Full Text:
    : ______________________________________________________ David S. Hurwitz Pedestrian distraction at roadway crossings has been correlated with a

  • Improving and Understanding Deep Models for Natural Language Comprehension

    Creator:
    Ghaeini, Reza
    Abstract:
    Natural Language Comprehension is a challenging domain of Natural Language Processing. To improve a model’s language comprehension/understanding, one approach would be to enrich the structure of the model to enhance its capability in learning the latent rules of the language. In this dissertation, we will first introduce several deep models...
    Resource Type:
    Dissertation
    Full Text:
    Categorical performance analyses (accuracy) of ESIM [11], DR-BiLSTM (DR(S)) and Ensemble DR-BiLSTM (DR(E)) on

  • Learning and Improving Policies for Probabilistic Planning Problems

    Creator:
    Issakkimuthu, Murugeswari
    Abstract:
    In this work, we study the problem of learning and improving policies for probabilistic planning problems. In the first part, we train neural network policies for probabilistic planning problems modeled as factored Markov decision problems. The objective is to train problem-specific neural networks via supervised learning to imitate the action...
    Resource Type:
    Dissertation
    Full Text:
    , Prof. N. S. Narayanaswamy and Prof. Deepak Khemani at IIT Madras, and Prof. Anselm Blumer at Tufts