SAOSDB : a structural active-object system debugger

Structural active-object systems (SAOSs) are transition-based object-oriented systems suitable for various concurrent applications. A SAOS consists of a collection of interacting active objects that can be structurally and hierarchically composed. The SAOS framework enables $AO$ application programs to be developed rapidly. However, nondeterministic behaviors of active object sometimes make debugging SAOS application programs difficult. To overcome this problem, Structural Active-Object System Debugger (SAOSDB) was designed and implemented as an object-based visual debugging tool. SAOSDB allows a programmer to see directly the states of the active objects and to control interactively the executions of the behavior specification statements of active objects. SAOSDB itself is implemented as a SAOS.Keyword and Phrases: object-oriented programming, object-based debugger, visual debugger, active-object system, structural composition, hierarchical composition1993 best estimate for issue date based on available information

A Graph-Based Semantics Workbench for Concurrent Asynchronous Programs

A number of novel programming languages and libraries have been proposed that offer simpler-to-use models of concurrency than threads. It is challenging, however, to devise execution models that successfully realise their abstractions without forfeiting performance or introducing unintended behaviours. This is exemplified by SCOOP---a concurrent object-oriented message-passing language---which has seen multiple semantics proposed and implemented over its evolution. We propose a "semantics workbench" with fully and semi-automatic tools for SCOOP, that can be used to analyse and compare programs with respect to different execution models. We demonstrate its use in checking the consistency of semantics by applying it to a set of representative programs, and highlighting a deadlock-related discrepancy between the principal execution models of the language. Our workbench is based on a modular and parameterisable graph transformation semantics implemented in the GROOVE tool. We discuss how graph transformations are leveraged to atomically model intricate language abstractions, and how the visual yet algebraic nature of the model can be used to ascertain soundness.Comment: Accepted for publication in the proceedings of FASE 2016 (to appear

Modular and composable extensions to smalltalk using composition filters

Current and future trends in computer science require extensions to Smalltalk. Rather than arguing for particular language mechanisms to deal with specific requirements, in this position paper we want to make a case for two requirements that Smalltalk extensions should fulfill. The first is that the extensions must be integrated with Smalltalk without violating its basic object model. The second requirement is that extensions should allow for defining objects that are still adaptable, extensible and reusable, and in particular do not cause inheritance anomalies. We propose the composition filters model as a framework for language extensions that fulfills these criteria. Its applicability to solving various modeling problems is briefly illustrated

Composing Software from Multiple Concerns: A Model and Composition Anomalies

Constructing software from components is considered to be a key requirement for managing the complexity of software. Separation of concerns makes only sense if the realizations of these concerns can be composed together effectively into a working program. Various publications have shown that composability of software is far from trivial and fails when components express complex behavior such as constraints, synchronization and history-sensitiveness. We believe that to address the composability problems, we need to understand and define the situations where composition fails. To this aim, in this paper we (a) introduce a general model of multi-dimensional concern composition, and (b) define so-called composition anomalies

Next generation software environments : principles, problems, and research directions

The past decade has seen a burgeoning of research and development in software environments. Conferences have been devoted to the topic of practical environments, journal papers produced, and commercial systems sold. Given all the activity, one might expect a great deal of consensus on issues, approaches, and techniques. This is not the case, however. Indeed, the term "environment" is still used in a variety of conflicting ways. Nevertheless substantial progress has been made and we are at least nearing consensus on many critical issues.The purpose of this paper is to characterize environments, describe several important principles that have emerged in the last decade or so, note current open problems, and describe some approaches to these problems, with particular emphasis on the activities of one large-scale research program, the Arcadia project. Consideration is also given to two related topics: empirical evaluation and technology transition. That is, how can environments and their constituents be evaluated, and how can new developments be moved effectively into the production sector

Logic programming in the context of multiparadigm programming: the Oz experience

Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This article has two goals: to give a tutorial of logic programming in Oz and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We conclude with lessons learned from this work, a brief history of Oz, and many entry points into the Oz literature.Comment: 48 pages, to appear in the journal "Theory and Practice of Logic Programming

A Study of Concurrency Bugs and Advanced Development Support for Actor-based Programs

The actor model is an attractive foundation for developing concurrent applications because actors are isolated concurrent entities that communicate through asynchronous messages and do not share state. Thereby, they avoid concurrency bugs such as data races, but are not immune to concurrency bugs in general. This study taxonomizes concurrency bugs in actor-based programs reported in literature. Furthermore, it analyzes the bugs to identify the patterns causing them as well as their observable behavior. Based on this taxonomy, we further analyze the literature and find that current approaches to static analysis and testing focus on communication deadlocks and message protocol violations. However, they do not provide solutions to identify livelocks and behavioral deadlocks. The insights obtained in this study can be used to improve debugging support for actor-based programs with new debugging techniques to identify the root cause of complex concurrency bugs.Comment: - Submitted for review - Removed section 6 "Research Roadmap for Debuggers", its content was summarized in the Future Work section - Added references for section 1, section 3, section 4.3 and section 5.1 - Updated citation