Behavior models for software architecture

Monterey Phoenix (MP) is an approach to formal software system architecture specification based on behavior models. Architecture modeling focuses not only on the activities and interactions within the system, but also on the interactions between the system and its environment, providing an abstraction for interaction specification. The behavior of the system is defined as a set of events (event trace) with two basic relations: precedence and inclusion. The structure of possible event traces is specified using event grammars and other constraints organized into schemas. The separation of the interaction description from the components behavior is an essential MP feature. The schema framework is amenable to stepwise architecture refinement, reuse, composition, visualization, and multiple view extraction. The approach yields a basis for executable architecture specification supporting early testing and verification, systematic use case generation, and performance estimates with automated tools.Consortium for Robotics and Unmanned Systems Education and Research (CRUSER)Consortium for Robotics and Unmanned Systems Education and Research (CRUSER)Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

Synthesizing Program Input Grammars

We present an algorithm for synthesizing a context-free grammar encoding the language of valid program inputs from a set of input examples and blackbox access to the program. Our algorithm addresses shortcomings of existing grammar inference algorithms, which both severely overgeneralize and are prohibitively slow. Our implementation, GLADE, leverages the grammar synthesized by our algorithm to fuzz test programs with structured inputs. We show that GLADE substantially increases the incremental coverage on valid inputs compared to two baseline fuzzers

On Language Processors and Software Maintenance

This work investigates declarative transformation tools in the context of software maintenance. Besides maintenance of the language specification, evolution of a software language requires the adaptation of the software written in that language as well as the adaptation of the software that transforms software written in the evolving language. This co-evolution is studied to derive automatic adaptations of artefacts from adaptations of the language specification. Furthermore, AOP for Prolog is introduced to improve maintainability of language specifications and derived tools.Die Arbeit unterstützt deklarative Transformationswerkzeuge im Kontext der Softwarewartung. Neben der Wartung der Sprachbeschreibung erfordert die Evolution einer Sprache sowohl die Anpassung der Software, die in dieser Sprache geschrieben ist als auch die Anpassung der Software, die diese Software transformiert. Diese Koevolution wird untersucht, um automatische Anpassungen von Artefakten von Anpassungen der Sprachbeschreibungen abzuleiten. Weiterhin wird AOP für Prolog eingeführt, um die Wartbarkeit von Sprachbeschreibungen und den daraus abgeleiteten Werkzeugen zu erhöhen

Doctor of Philosophy

dissertationDomain-specific languages (DSLs) are increasingly popular, and there are a variety of ways to create a DSL. A DSL designer might write an interpreter from scratch, compile the DSL to another language, express DSL concepts using only the existing forms of an existing language, or implement DSL constructs using a language's extension capabilities, including macros. While extensible languages can offer the easiest opportunity for creating a DSL that takes advantage of the language's existing infrastructure, existing tools for debugging fail to adequately adapt the debugging experience to a given domain. This dissertation addresses the problem of debugging DSLs defined with macros and describes an event-oriented approach that works well with a macro-expansion view of language implementation. It pairs the mapping of DSL terms to host terms with an event mapping to convert primitive events back to domain-specific concepts. Domain-specific events can be further inspected or manipulated to construct domain-specific debuggers. This dissertation presents a core model of evaluation and events and also presents a language design-analogous to pattern-based notations for macros, but in the other direction-for describing how events in a DSL's expansion are mapped to events at the DSL's level. The domain-specific events can enable useful, domain-specific debuggers, and the dissertation introduces a design for a debugging framework to help with debugger construction. To validate the design of the debugging framework, a debugging framework, Ripple, is implemented, and this dissertation demonstrates that with a modest amount of work, Ripple can support building domain-specific debuggers

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

A Survey on the Automated Analyses of Feture Models

Feature models are one of the most important assets in software product line engineering when capturing variability. Although the analyses of feature models was stated as challenging problem since they were fist proposed, it has not been fully achieved yet. in this paper we survey the state of the art on the automated analyses of feature models outlining the main advances made until now. We conclude that our proposal based on constraint programming is one of the most promising ones since it supports all the operations identified in this paper over feature models.Ministerio de Educación y Ciencia TIC2003-02737-C02-0

Towards the Humanisation of Programming Tool Interactions

Program analysis tools, from simple static semantic analysis by a compiler, to complex dynamic analyses of data flow and security, have become commonplace in modern day programming. Many of the simpler analyses, such as the afore- mentioned compiler checking or linters designed to enforce code style, may even go unnoticed or unconsidered by most users, ubiquitous as they are. Despite this, and despite the obvious utility that such programming tools can provide, many warnings provided by them go unheeded by programmers most of the time.There are several reasons for this phenomenon: the propensity to produce false positives undermines confidence in the validity of warnings, the tools do not in- tegrate well into the normal workflow of the developer, sometimes the warning message is simply too esoteric for most users to understand, and so on. A com- mon theme can be drawn from these reasons for ignoring the often-times very useful information given by a programming tool: the tool itself is difficult to use.In this thesis, we consider ways in which we can bridge this gap between users and tools. To do this, we draw from observations about the way in which we interact with each other in the most basic human-to-human context. Applying these lessons to a human-tool interaction allow us to examine ways in which tools may be deficient, and investigate methods for making the interaction more natural and human-like.We explore this issue by framing the interaction as a "conversation" between a human and their development environment. We then present a new programming tool, Progger, built using design principles driven by the "conversational lens" which we use to look at these interactions. After this, we present a user study using a novel low-cost methodology, aimed at evaluating the efficacy of the Progger tool. From the results of this user study, we present a new, more streamlined version of Progger, and finally investigate the way in which it can be used to direct the users attention when conducting a code comprehension exercise