Thesis (M.S.)--University of Tennessee, Knoxville, 2003. / Title from title page screen (viewed Sept. 23, 2003). Thesis advisor: Christopher Pionke. Document formatted into pages (xv, 314 p. : ill. (some col.)). Vita. Includes bibliographical references (p. 137-139).

As software systems grow in complexity, the need for efficient automated techniques for design, testing and verification becomes more and more critical. Specification-based testing provides an effective approach for checking the correctness of software in general. Constraint-based analysis using specifications enables checking various rich properties by automating generation of test inputs. However, as specifications get more complex, existing analyses face a scalability problem due to state explosion. This dissertation introduces a novel approach to analyze declarative specifications incrementally; presents a constraint prioritization and partitioning methodology to enable efficient incremental analyses; defines a suite of optimizations to improve the analyses further; introduces a novel approach for testing software product lines; and provides an experimental evaluation that shows the feasibility and scalability of the approach. The key insight behind the incremental technique is declarative slicing, which is a new class of optimizations. The optimizations are inspired by traditional program slicing for imperative languages but are applicable to analyzable declarative languages, in general, and Alloy, in particular. We introduce a novel algorithm for slicing declarative models. Given an Alloy model, our fully automatic tool, Kato, partitions the model into a base slice and a derived slice using constraint prioritization. As opposed to the conventional use of the Alloy Analyzer, where models are analyzed as a whole, we perform analysis incrementally, i.e., using several steps. A satisfying solution to the base slice is systematically extended to generate a solution for the entire model, while unsatisfiability of the base implies unsatisfiability of the entire model. We show how our incremental technique enables different analysis tools and solvers to be used in synergy to further optimize our approach. Compared to the conventional use of the Alloy Analyzer, this means even more overall performance enhancements for solving declarative models. Incremental analyses have a natural application in the software product line domain. A product line is a family of programs built from features that are increments in program functionality. Given properties of features as firstorder logic formulas, we automatically generate test inputs for each product in a product line. We show how to map a formula that specifies a feature into a transformation that defines incremental refinement of test suites. Our experiments using different data structure product lines show that our approach can provide an order of magnitude speed-up over conventional techniques. / text

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A data structure editor is presented for use in an integrated, fragment-based programming environment. This editor employs high resolution computer graphics to present the user with an iconic representation of the internal storage of a running program. / The editor allows the creation, modification, and deletion of data structures. These abilities allow the user to quickly sketch data structures with which to test incomplete program fragments, alleviating the need for driver routines. / To keep the user cognizant of events inside his program, a technique for automated display management is presented allowing the user to keep the most important objects in the viewport at all times. A history facility permits the user to see the former values of all variables. / Execution controls are provided allowing the user to control the scope and speed of execution, manipulate frames on the run-time stack, set breakpoints, and profile the executing algorithm.

This thesis implements Milton Babbitt's time-point system for music composition via the creation of a computer software application for the Macintosh computer. This system asks the composer to enter musical information, which is used to calculate pitch, duration, articulation, texture, octave, and silence, for the time-point composition. The application generates a file of musical information that is compatible with a performance application; the performance application will execute the composition communicating with MIDI-compatible musical instruments.The purpose of this project was to create a compositional tool that would implement the time-point system by reducing hours of hand calculation and tedium, and would provide an accessible and efficient approach to time-point composition. It is intended that this application be used to assist both the serious composer as well as the student of music composition. / School of Music

Metacognition in collaborative learning settings is an emerging topic in research on metacognition. This study focused on the development of metacognitive skills during pair programming in order to enhance knowledge productivity. Pair programming entails collaboration between two programmers, each fulfilling a specific role during the execution of the programming task, and has recently been used in educational contexts as a teaching and learning strategy to improve learning in computer programming (DeClue, 2003:49; Preston, 2006b:16). In this study the outcomes of collaborative learning during pair programming are referred to as knowledge productivity, which is measured against the criteria of enhanced knowledge construction, enhanced problem-solving skills, conceptual changes in individual perspectives, commitment to work with the other member of the pair, and application of the results of the learning process (Tillema & Van der Westhuizen, 2006:51,55). To investigate the influence of a metacognitive teaching and learning approach to pair programming on knowledge productivity, a repeated-measures experiment with a control group was conducted among a number of Grade 11 learners who had been implementing pair programming in the learning of Information Technology at secondary school level. At the beginning of the study both groups completed two questionnaires, one on metacognition and the other on knowledge productivity. At the same stage, a number of randomly selected participants from each group were interviewed on the same two topics, using a semi-structured interview approach. This procedure was repeated three months later, without any intervention. During the intervention phase, the experimental group implemented a set of self-directed metacognitive questions that they had to answer while doing a pair programming task. These self-directed metacognitive questions form part of a metacognitive teaching and learning strategy suggested by the researcher. The members of the experimental group also kept a journal to describe how the self-directed questions directed their thinking during the execution of the pair programming task. At the end of the experimental period, the mo questionnaires on metacognition and knowledge productivity were completed by both groups again, supported by the interviews of the same selected participants. The results derived from the empirical study showed that the knowledge productivity of learners programming in pairs can be enhanced by implementing a metacognitive teaching and learning strategy. The metacognitive teaching and learning strategy implemented during this study was then adapted to make it suitable for use by teachers and learners during pair programming. / Thesis (Ph.D. (Education)--North-West University, Potchefstroom Campus, 2010.

Metacognition in collaborative learning settings is an emerging topic in research on metacognition. This study focused on the development of metacognitive skills during pair programming in order to enhance knowledge productivity. Pair programming entails collaboration between two programmers, each fulfilling a specific role during the execution of the programming task, and has recently been used in educational contexts as a teaching and learning strategy to improve learning in computer programming (DeClue, 2003:49; Preston, 2006b:16). In this study the outcomes of collaborative learning during pair programming are referred to as knowledge productivity, which is measured against the criteria of enhanced knowledge construction, enhanced problem-solving skills, conceptual changes in individual perspectives, commitment to work with the other member of the pair, and application of the results of the learning process (Tillema & Van der Westhuizen, 2006:51,55). To investigate the influence of a metacognitive teaching and learning approach to pair programming on knowledge productivity, a repeated-measures experiment with a control group was conducted among a number of Grade 11 learners who had been implementing pair programming in the learning of Information Technology at secondary school level. At the beginning of the study both groups completed two questionnaires, one on metacognition and the other on knowledge productivity. At the same stage, a number of randomly selected participants from each group were interviewed on the same two topics, using a semi-structured interview approach. This procedure was repeated three months later, without any intervention. During the intervention phase, the experimental group implemented a set of self-directed metacognitive questions that they had to answer while doing a pair programming task. These self-directed metacognitive questions form part of a metacognitive teaching and learning strategy suggested by the researcher. The members of the experimental group also kept a journal to describe how the self-directed questions directed their thinking during the execution of the pair programming task. At the end of the experimental period, the mo questionnaires on metacognition and knowledge productivity were completed by both groups again, supported by the interviews of the same selected participants. The results derived from the empirical study showed that the knowledge productivity of learners programming in pairs can be enhanced by implementing a metacognitive teaching and learning strategy. The metacognitive teaching and learning strategy implemented during this study was then adapted to make it suitable for use by teachers and learners during pair programming. / Thesis (Ph.D. (Education)--North-West University, Potchefstroom Campus, 2010.

Real-time systems are amongst the most safety critical systems involving computer software and the incorrect functioning of this software can cause great damage, up to and including the loss of life. If seems sensible therefore to write real-time software in a way that gives us the best chance of correctly implementing specifications. Because of the high level of functional programming languages, their semantic simplicity and their amenability to formal reasoning and correctness preserving transformation it thus seems natural to use a functional language for this task. This thesis explores the problems of applying functional programming languages to real-time by defining the real-time functional programming language Ruth. The first part of the thesis concerns the identification of the particular problems associated with programming real-time systems. These can broadly be stated as a requirement that a real-time language must be able to express facts about time, a feature we have called time expressibility. The next stage is to provide time expressibility within a purely functional framework. This is accomplished by the use of timestamps on inputs and outputs and by providing a real-time clock as an input to Ruth programs. The final major part of the work is the construction of a formal definition of the semantics of Ruth to serve as a basis for formal reasoning and transformation. The framework within which the formal semantics of a real-time language are defined requires time expressibility in the same way as the real-time language itself. This is accomplished within the framework of domain theory by the use of specialised domains for timestamped objects, called herring-bone domains. These domains could be used as the basis for the definition of the semantics of any real-time language.

Typescript. / Thesis (Ph. D.)--University of Hawaii at Manoa, 1974. / Bibliography: leaves [112]-114. / iv, 114 leaves

Algorithms that learn sets of rules describing a concept from its examples have been widely studied in machine learning and have been applied to problems in medicine, molecular biology, planning and linguistics. Many of these algorithms used a separate-and-conquer strategy, repeatedly searching for rules that explain different parts of the example set. When examples are scarce, however, it is difficult for these algorithms to evaluate the relative quality of two or more rules which fit the examples equally well. This dissertation proposes, implements and examines a general technique for modifying rule evaluation in order to improve learning performance in these situations. This approach, called Description-based Evaluation Function Transfer (DEFT), adjusts the way rules are evaluated on a target concept by taking into account the performance of similar rules on a related support task that is supplied by a domain expert. Central to this approach is a novel theory of task similarity that is defined in terms of syntactic properties of rules, called descriptions, which define what it means for rules to be similar. Each description is associated with a prior distribution over classification probabilities derived from the support examples and a rule's evaluation on a target task is combined with the relevant prior using Bayes' rule. Given some natural conditions regarding the similarity of the target and support task, it is shown that modifying rule evaluation in this way is guaranteed to improve estimates of the true classification probabilities. Algorithms to efficiently implement Deft are described, analysed and used to measure the effect these improvements have on the quality of induced theories. Empirical studies of this implementation were carried out on two artificial and two real-world domains. The results show that the inductive transfer of evaluation bias based on rule similarity is an effective and practical way to improve learning when training examples are limited.