This thesis reports on research undertaken in an exploration of the use of formal semantics for student modelling in intelligent tutoring systems. The domain chosen was that of tutoring programming languages and within that domain Prolog was selected to be the target language for this exploration. The problem considered is one of how to analyse students' errors at a level which allows diagnosis to be more flexible and meaningful than is possible with the 'mal-rules' and 'bugcatalogue' approach of existing systems. The ideas put forward by Robin Milner [1980] in his Calculus of Communicating Systems (CCS) form the basis of the formalism which is proposed as a solution to this problem. Based on the findings of an empirical investigation, novices' misconceptions of control flow in Prolog was defined as a suitable area in which to explore the application of this solution. A selection of Prolog programs used in that investigation was formally described in terms of CCS. These formal descriptions were used by a production rule system to generate a number of the incomplete or faulty models of Prolog execution which were identified in the first empirical study. In a second empirical study, a machine-analysis tool, designed to be part of a diagnostic tutoring module, used these models to diagnose students' misconceptions of Prolog control flow. This initial application of CCS to student modelling showed that the models of Prolog execution generated by the system could be used successfully to detect students' misunderstandings. Results from the research reported here indicate that the use of formal semantics to model programming languages has a useful contribution to make to the task of student modelling.

Scientists are trending towards usage of high-level programming languages such as Python. The convenience of these languages often have a performance cost. As the amount of data being processed increases this can make using these languages unfeasible. Parallelism is a means to achieve better performance, but many users are unaware of it, or find it difficult to work with. This thesis presents ParForPy, a means for loop-parallelization to to simplify usage of parallelism in Python for users. Discussion is included for determining when parallelism matches well with the problem. Results are given that indicate that ParForPy is both capable of improving program execution time and perceived to be a simpler construct to understand than other techniques for parallelism in Python.

This thesis investigates nondeterminism in functional programming languages. To establish a precise understanding of nondeterministic language properties, Sondergaard and Sestoft's analysis and definitions of functional language properties are adopted as are the characterizations of weak and strong nondeterminism. This groundwork is followed by a denotational semantic description of a nondeterministic language (suggested by Sondergaard and Sestoft). In this manner, a precise characterization of the effects of strong nondeterminism is developed. Methods used to hide nondeterminism to in order to overcome or sidestep the problem of strong nondeterminism in pure functional languages are defined. These different techniques ensure that functional languages remain pure but also include some of the advantages of nondeterminism. Lastly, this discussion of nondeterminism is applied to the area of functional parallel language implementation to indicate that the related problem and the possible solutions are not purely academic. This application gives rise to an interesting discussion on optimization of list parallelism. This technique relies on the ability to decide when a bag may be used instead of a list.

Static analysis is the name given to a number of compile time analysis techniques used to automatically generate information which can lead to improvements in the execution performance of function languages. This thesis provides an introduction to these techniques and their implementation. The abstract interpretation framework is an example of a technique used to extract information from a program by providing the program with an alternate semantics and evaluating this program over a non-standard domain. The elements of this domain represent certain properties of interest. This framework is examined in detail, as well as various extensions and variants of it. The use of binary logical relations and program logics as alternative formulations of the framework , and partial equivalence relations as an extension to it, are also looked at. The projection analysis framework determines how much of a sub-expression can be evaluated by examining the context in which the expression is to be evaluated, and provides an elegant method for finding particular types of information from data structures. This is also examined. The most costly operation in implementing an analysis is the computation of fixed points. Methods developed to make this process more efficient are looked at. This leads to the final chapter which highlights the dependencies and relationships between the different frameworks and their mathematical disciplines. / KMBT_223

Yes / This technical report describes one technique for implementing π-calculus style actions in a programming language. It first attempts to clarify the nature of actions, then gives requirements a primitive in a programming language must satisfy if it is to be used as the basis for the implementation of actions. Finally an example is given of how actions may be implemented in Ada.

No description available.

As software projects become larger and more complex, software optimization at that scale is only feasible through automated means. One such component of software optimization is alias analysis, which attempts to determine which variables in a program refer to the same area in memory, and is used to relocate instructions to improve performance without interfering with program execution. Several alias analyses have been proposed over the past few decades, with varying degrees of precision and time and space complexity, but few studies have been conducted to compare these techniques with one another, nor to measure with program data to confirm their accuracy. Normally, this is out of the scope of alias analyses because these processes are static, and can only rely upon the input source code. We address these limitations by instrumenting several benchmarks and combining their data with commonly used alias analyses to objectively measure the accuracy of those analyses. Additionally, we also gather additional program statistics to further determine which programs are the most suitable for evaluating subsequent alias analysis techniques.

No description available.

No description available.

No description available.