Type classes, first proposed during the design of the Haskell programming language, extend standard type systems to support overloaded functions. Since their introduction, type classes have been used to address a range of problems, from typing ordering and arithmetic operators to describing heterogeneous lists and limited subtyping. However, while type class programming is useful for a variety of practical problems, its wider use is limited by the inexpressiveness and hidden complexity of current mechanisms. We propose two improvements to existing class systems. First, we introduce several novel language features, instance chains and explicit failure, that increase the expressiveness of type classes while providing more direct expression of current idioms. To validate these features, we have built an implementation of these features, demonstrating their use in a practical setting and their integration with type reconstruction for a Hindley-Milner type system. Second, we define a set-based semantics for type classes that provides a sound basis for reasoning about type class systems, their implementations, and the meanings of programs that use them.

Software systems are growing ever larger. Early software systems were singular units developed by small teams of programmers writing in the same programming language. Modern software systems, on the other hand, consist of numerous interoperating components written by different teams and in different programming languages. While this more modular and diversified approach to software development has enabled us to build ever larger and more complex software systems, it has, however, made it harder to ensure the reliability and security of software systems. In this thesis we study and remedy the security flaws that arise when attempting to resolve the difference in abstractions between components written in high-level functional programming languages and components written in imperative low-level programming languages. High-level functional programming languages, treat computation as the evaluation of mathematical functions. Low-level imperative programming languages, on the contrary, provide programmers with features that enable them to directly interact with the underlying hardware. While these features help programmers write more efficient software, they also make it easy to write malware through techniques such as buffer overflows and return oriented programming. Concretely, we develop new run-time approaches for protecting components written in functional programming languages from malicious components written in low-level programming languages by making using of an emerging memory isolation mechanism.This memory isolation mechanism is called the Protected Module Architecture (PMA). Informally, PMA isolates the code and data that reside within a certain area of memory by restricting access to that area based on the location of the program counter. We develop these run-time protection techniques that make use of PMA for three important areas where components written in functional programming languages are threatened by malicious low-level components: foreign function interfaces, abstract machines and compilation. In everyone of these three areas, we formally prove that our run-time protection techniques are indeed secure. In addtion to that we also provide implementations of our ideas through a fully functional compiler and a well-performing abstract machine.

Functional languages offer a number of advantages over their imperative counterparts. However, a substantial amount of the time spent on processing functional programs is due to the large amount of storage management which must be performed. Two apparent reasons for this are that the programmer is prevented from including explicit storage management operations in programs which have a purely functional semantics, and that more readable programs are often far from optimal in their use of storage. Correspondingly, two alternative approaches to the optimisation of store usage at compile-time are presented in this thesis. The first approach is called compile-time garbage collection. This approach involves determining at compile-time which cells are no longer required for the evaluation of a program, and making these cells available for further use. This overcomes the problem of a programmer not being able to indicate explicitly that a store cell can be made available for further use. Three different methods for performing compile-time garbage collection are presented in this thesis; compile-time garbage marking, explicit deallocation and destructive allocation. Of these three methods, it is found that destructive allocation is the only method which is of practical use. The second approach to the optimisation of store usage is called compile-time garbage avoidance. This approach involves transforming programs into semantically equivalent programs which produce less garbage at compile-time. This attempts to overcome the problem of more readable programs being far from optimal in their use of storage. In this thesis, it is shown how to guarantee that the process of compile-time garbage avoidance will terminate. Both of the described approaches to the optimisation of store usage make use of the information obtained by usage counting analysis. This involves counting the number of times each value in a program is used. In this thesis, a reference semantics is defined against which the correctness of usage counting analyses can be proved. A usage counting analysis is then defined and proved to be correct with respect to this reference semantics. The information obtained by this analysis is used to annotate programs for compile-time garbage collection, and to guide the transformation when compile-time garbage avoidance is performed. It is found that compile-time garbage avoidance produces greater increases in efficiency than compile-time garbage collection, but much of the garbage which can be collected by compile-time garbage collection cannot be avoided at compile-time. The two approaches are therefore complementary, and the expressions resulting from compile-time garbage avoidance transformations can be annotated for compile-time garbage collection to further optimise the use of storage.

In this thesis is presented design and implementation of a system performing genetic programming in simply typed lambda calculus. Population initialization method based on term generating technique producing typed lambda terms in long normal form is introduced. This method is parameterized by simple search strategy. Several search strategies are presented, such as strategy for systematic generation or strategy corresponding to standard ramped half-and-half method. Another such a strategies called \textit{geometric} strategy is further examined in experiments and shown to have various desirable effects such as improved success rate, lesser time consumption and smaller average term size in comparison with standard ramped half-and-half generating method. Other performance enhancements are proposed and supported by experiments such as eta-normalization of generated individuals and @-tree representation of individuals. Abstraction elimination is utilized to enable use of simple tree- swapping crossover. Powered by TCPDF (www.tcpdf.org)

Moore’s law reaching its physical limitations has pushed the industry to produce multicore processors. However, programming those processors with an imperative language is not easy since it requires developers to create and synchronize threads. A pure functional language is an adequate tool for this task both from the architectural point of view and from the developer’s. We will show that an architecture can benefit from the implicit parallelism present on functional programs and from the lack of side effects making it easier to parallelize. The developer benefits from functional languages from the superior expressiveness of the language to avoid bugs. In this dissertation, we present the ACQuA architecture, a multicore accelerator created to explore parallelism available in function calls from a pure functional program. ACQuA uses hardware support and a specificallytailored memory organization to minimize the overheads of scheduling, communication, and synchronization. Function calls are placed into a queue and are scheduled to different processing units. The processing units are interconnected and exchange results from function applications. In this work we defined a high level model of the accelerator and how to compile a functional program to it. We also simulated the accelerator and evaluated results, such as speedup, memory usage, and communication overhead of the proposed architecture. We defined the necessary traits of a program to achieve a good speedup on the architecture. On the ideal use case, we can increase the speed up at the same rate we increase the number of processing units in the architecture.

Operating systems software is fundamental to modern computer systems: all other applications are dependent upon the correct and timely provision of basic system services. At the same time, advances in programming languages and type theory have lead to the creation of functional programming languages with type systems that are designed to combine theorem proving with practical systems programming. The Terrier operating system project focuses on low-level systems programming in the context of a multi-core, real-time, embedded system, while taking advantage of a dependently typed programming language named ATS to improve reliability. Terrier is a new point in the design space for an operating system, one that leans heavily on an associated programming language, ATS, to provide safety that has traditionally been in the scope of hardware protection and kernel privilege. Terrier tries to have far fewer abstractions between program and hardware. The purpose of Terrier is to put programs as much in contact with the real hardware, real memory, and real timing constraints as possible, while still retaining the ability to multiplex programs and provide for a reasonable level of safety through static analysis.

The detection of similar code can support many so ware engineering tasks such as program understanding and program classification. Many excellent approaches have been proposed to detect programs having similar syntactic features. However, these approaches are unable to identify programs dynamically or statistically close to each other, which we call behaviorally similar programs. We believe the detection of behaviorally similar programs can enhance or even automate the tasks relevant to program classification. In this thesis, we will discuss our current approaches to identify programs having similar behavioral features in multiple perspectives. We first discuss how to detect programs having similar functionality. While the definition of a program’s functionality is undecidable, we use inputs and outputs (I/Os) of programs as the proxy of their functionality. We then use I/Os of programs as a behavioral feature to detect which programs are functionally similar: two programs are functionally similar if they share similar inputs and outputs. This approach has been studied and developed in the C language to detect functionally equivalent programs having equivalent I/Os. Nevertheless, some natural problems in Object Oriented languages, such as input generation and comparisons between application-specific data types, hinder the development of this approach. We propose a new technique, in-vivo detection, which uses existing and meaningful inputs to drive applications systematically and then applies a novel similarity model considering both inputs and outputs of programs, to detect functionally similar programs. We develop the tool, HitoshiIO, based on our in-vivo detection. In the subjects that we study, HitoshiIO correctly detect 68.4% of functionally similar programs, where its false positive rate is only 16.6%. In addition to functional I/Os of programs, we attempt to discover programs having similar execution behavior. Again, the execution behavior of a program can be undecidable, so we use instructions executed at run-time as a behavioral feature of a program. We create DyCLINK, which observes program executions and encodes them in dynamic instruction graphs. A vertex in a dynamic instruction graph is an instruction and an edge is a type of dependency between two instructions. The problem to detect which programs have similar executions can then be reduced to a problem of solving inexact graph isomorphism. We propose a link analysis based algorithm, LinkSub, which vectorizes each dynamic instruction graph by the importance of every instruction, to solve this graph isomorphism problem efficiently. In a K Nearest Neighbor (KNN) based program classification experiment, DyCLINK achieves 90 + % precision. Because HitoshiIO and DyCLINK both rely on dynamic analysis to expose program behavior, they have better capability to locate and search for behaviorally similar programs than traditional static analysis tools. However, they suffer from some common problems of dynamic analysis, such as input generation and run-time overhead. These problems may make our approaches challenging to scale. Thus, we create the system, Macneto, which integrates static analysis with machine topic modeling and deep learning to approximate program behaviors from their binaries without truly executing programs. In our deobfuscation experiments considering two commercial obfuscators that alter lexical information and syntax in programs, Macneto achieves 90 + % precision, where the groundtruth is that the behavior of a program before and after obfuscation should be the same. In this thesis, we offer a more extensive view of similar programs than the traditional definitions. While the traditional definitions of similar programs mostly use static features, such as syntax and lexical information, we propose to leverage the power of dynamic analysis and machine learning models to trace/collect behavioral features of pro- grams. These behavioral features of programs can then apply to detect behaviorally similar programs. We believe the techniques we invented in this thesis to detect behaviorally similar programs can improve the development of software engineering and security applications, such as code search and deobfuscation.

<p> </p><p>In this paper I present the domain specific language HaGPipe for graphics programming in Haskell. HaGPipe has a clean, purely functional and strongly typed interface and targets the whole graphics pipeline including the programmable shaders of the GPU. It can be extended for use with various backends and this paper provides two different ones. The first one generates vertex and fragment shaders in Cg for the GPU, and the second one generates vertex shader code for the SPUs on PlayStation 3. I will demonstrate HaGPipe's many capabilities of producing optimized code, including an extensible rewrite rule framework, automatic packing of vertex data, common sub expression elimination and both automatic basic block level vectorization and loop vectorization through the use of structures of arrays.</p>

The benefits of programming in a functional style are well known. In particular, algorithms that are expressed as compositions of functions operating on series/vectors/streams of data elements are much easier to understand and modify than equivalent algorithms expressed as loops. Unfortunately, many programmers hesitate to use series expressions, because they are typically implemented very inefficiently. Common Lisp macro packages (OSS) has been implemented which supports a restricted class of series expressions, obviously synchronizable series expressions, which can be evaluated very efficiently by automatically converting them into loops. Using this macro package, programmers can obtain the advantages of expressing computations as series expressions without incurring any run-time overhead.

The benefits of programming in a functional style are well known. In particular, algorithms that are expressed as compositions of functions operating on series/vectors/streams of data elements are much easier to understand and modify than equivalent algorithms expressed as loops. Unfortunately, many programmers hesitate to use series expressions, because they are typically implemented very inefficiently---the prime source of inefficiency being the creation of intermediate series objects. A restricted class of series expressions, obviously synchronizable series expressions, is defined which can be evaluated very efficiently. At the cost of introducing restrictions which place modest limits on the series expressions which can be written, the restrictions guarantee that the creation of intermediate series objects is never necessary. This makes it possible to automatically convert obviously synchronizable series expressions into highly efficient loops using straight forward algorithms.