Object-oriented domain specific compilers for programming FPGAs

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An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Intelligent systems engineering with reconfigurable computing

Intelligent computing systems comprising microprocessor cores, memory and reconfigurable user-programmable logic represent a promising technology which is well-suited for applications such as digital signal and image processing, cryptography and encryption, etc. These applications employ frequently recursive algorithms which are particularly appropriate when the underlying problem is defined in recursive terms and it is difficult to reformulate it as an iterative procedure. It is known, however, that hardware description languages (such as VHDL) as well as system-level specification languages (such as Handel-C) that are usually employed for specifying the required functionality of reconfigurable systems do not provide a direct support for recursion. In this paper a method allowing recursive algorithms to be easily described in Handel-C and implemented in an FPGA (field-programmable gate array) is proposed. The recursive search algorithm for the knapsack problem is considered as an exampleApplications in Artificial Intelligence - Knowledge EngineeringRed de Universidades con Carreras en Informática (RedUNCI

Custom Integrated Circuits

Contains reports on nine research projects.Analog Devices, Inc.International Business Machines CorporationJoint Services Electronics Program Contract DAAL03-89-C-0001U.S. Air Force - Office of Scientific Research Contract AFOSR 86-0164BDuPont CorporationNational Science Foundation Grant MIP 88-14612U.S. Navy - Office of Naval Research Contract N00014-87-K-0825American Telephone and TelegraphDigital Equipment CorporationNational Science Foundation Grant MIP 88-5876

On Oracles for Automated Diagnosis and Repair of Software Bugs

This HDR focuses on my work on automatic diagnosis and repair done over the past years. Among my past publications, it highlights three contributions on this topic, respectively published in ACM Transactions on Software Engineering and Methodology (TOSEM), IEEE Transactions on Software Engineering (TSE) and Elsevier Information & Software Technology (IST). My goal is to show that those three contributions share something deep, that they are founded on a unifying concept, which is the one of oracle. The first contribution is about statistical oracles. In the context of object-oriented software, we have defined a notion of context and normality that is specific to a fault class: missing method calls. Those inferred regularities act as oracle and their violations are considered as bugs. The second contribution is about test case based oracles for automatic repair. We describe an automatic repair system that fixes failing test cases by generating a patch. It is founded on the idea of refining the knowledge given by the violation of the oracle of the failing test case into finer-grain information, which we call a “micro-oracle”. By considering micro-oracles, we are capable of obtaining at the same time a precise fault localization diagnostic and a well-formed input-output specification to be used for program synthesis in order to repair a bug. The third contribution discusses a novel generic oracle in the context of exception handling. A generic oracle states properties that hold for many domains. Our technique verifies the compliance to this new oracle using test suite execution and exception injection. This document concludes with a research agenda about the future of engineering ultra-dependable and antifragile software systems

Data Understanding Applied to Optimization

The goal of this research is to explore and develop software for supporting visualization and data analysis of search and optimization. Optimization is an ever-present problem in science. The theory of NP-completeness implies that the problems can only be resolved by increasingly smarter problem specific knowledge, possibly for use in some general purpose algorithms. Visualization and data analysis offers an opportunity to accelerate our understanding of key computational bottlenecks in optimization and to automatically tune aspects of the computation for specific problems. We will prototype systems to demonstrate how data understanding can be successfully applied to problems characteristic of NASA's key science optimization tasks, such as central tasks for parallel processing, spacecraft scheduling, and data transmission from a remote satellite

Efficient Reasoning Techniques for Large Scale Feature Models

In Software Product Lines (SPLs), a feature model can be used to represent the similarities and differences within a family of software systems. This allows describing the systems derived from the product line as a unique combination of the features in the model. What makes feature models particularly appealing is the fact that the constraints in the model prevent incompatible features from being part of the same product. Despite the benefits of feature models, constructing and maintaining these models can be a laborious task especially in product lines with a large number of features and constraints. As a result, the study of automated techniques to reason on feature models has become an important research topic in the SPL community in recent years. Two techniques, in particular, have significant appeal for researchers: SAT solvers and Binary Decision Diagrams (BDDs). Each technique has been applied successfully for over four decades now to tackle many practical combinatorial problems in various domains. Currently, several approaches have proposed the compilation of feature models to specific logic representations to enable the use of SAT solvers and BDDs. In this thesis, we argue that several critical issues related to the use of SAT solvers and BDDs have been consistently neglected. For instance, satisfiability is a well-known NP-complete problem which means that, in theory, a SAT solver might be unable to check the satisfiability of a feature model in a feasible amount of time. Similarly, it is widely known that the size of BDDs can become intractable for large models. At the same time, we currently do not know precisely whether these are real issues when feature models, especially large ones, are compiled to SAT and BDD representations. Therefore, in our research we provide a significant step forward in the state-of-the-art by examining deeply many relevant properties of the feature modeling domain and the mechanics of SAT solvers and BDDs and the sensitive issues related to these techniques when applied in that domain. Specifically, we provide more accurate explanations for the space and/or time (in)tractability of these techniques in the feature modeling domain, and enhance the algorithmic performance of these techniques for reasoning on feature models. The contributions of our work include the proposal of novel heuristics to reduce the size of BDDs compiled from feature models, several insights on the construction of efficient domain-specific reasoning algorithms for feature models, and empirical studies to evaluate the efficiency of SAT solvers in handling very large feature models