The KB paradigm and its application to interactive configuration

The knowledge base paradigm aims to express domain knowledge in a rich formal language, and to use this domain knowledge as a knowledge base to solve various problems and tasks that arise in the domain by applying multiple forms of inference. As such, the paradigm applies a strict separation of concerns between information and problem solving. In this paper, we analyze the principles and feasibility of the knowledge base paradigm in the context of an important class of applications: interactive configuration problems. In interactive configuration problems, a configuration of interrelated objects under constraints is searched, where the system assists the user in reaching an intended configuration. It is widely recognized in industry that good software solutions for these problems are very difficult to develop. We investigate such problems from the perspective of the KB paradigm. We show that multiple functionalities in this domain can be achieved by applying different forms of logical inferences on a formal specification of the configuration domain. We report on a proof of concept of this approach in a real-life application with a banking company. To appear in Theory and Practice of Logic Programming (TPLP).Comment: To appear in Theory and Practice of Logic Programming (TPLP

Diagnosing interoperability problems and debugging models by enhancing constraint satisfaction with case -based reasoning

Modeling, Diagnosis, and Model Debugging are the three main areas presented in this dissertation to automate the process of Interoperability Testing of networking protocols. The dissertation proposes a framework that uses the Constraint Satisfaction Problem (CSP) paradigm to define a modeling language and problem solving mechanism for interoperability testing, and uses Case-Based Reasoning (CBR) for debugging interoperability test cases. The dissertation makes three primary contributions: (1) Definition of a new modeling language using CSP and Object-Oriented Programming. This language is simple, declarative, and transparent. It provides a tool for testers to implement models of interoperability test cases. The dissertation introduces the notions of metavariables, metavalues and optional metavariables to improve the modeling language capabilities. It proposes modeling of test cases from test suite specifications that are usually used in interoperability testing performed manually by testers. Test suite specifications are written by organizations or individuals and break down the testing into modules of test cases that make diagnosis of problems more meaningful to testers. (2) Diagnosis of interoperability problems using search supplemented by consistency inference methods in a CSP context to support explanations of the problem solving behavior. These methods are adapted to the OO-based CSP context. Testers can then generate reports for individual test cases and for test groups from a test suite specification. (3) Detection and debugging of incompleteness and incorrectness in CSP models of interoperability test cases. This is done through the integration of two modes of reasoning, namely CBR and CSP. CBR manages cases that store information about updating models as well as cases that are related to interoperability problems where diagnosis fails to generate a useful explanation. For the latter cases, CBR recalls previous similar useful explanations

Efficient Combination of Decision Procedure for MUS Computation

International audienceIn recent years, the problem of extracting a MUS (Minimal Unsatisfiable Subformula) from an unsatisfiable CNF has received much attention. Indeed, when a Boolean formula is proved unsatisfiable, it does not necessarily mean that all its clauses take part to the inconsistency; a small subset of them can be conflicting and make the whole set without any solution. Localizing a MUS can thus be extremely valuable, since it enables to circumscribe a minimal set of constraints that represents a cause for the infeasibility of the CNF. In this paper, we introduce a novel, original framework for computing a MUS. Whereas most of the existing approaches are based on complete algorithms, we propose an approach that makes use of both local and complete searches. Our combination is empirically evaluated against the current best techniques on a large set of benchmarks

A History-based Approach for Model Repair Recommendations in Software Engineering

Software is an everyday companion in today’s technology society that need to be evolved and maintained over long time periods. To manage the complexity of software projects, it has always been an effort to increase the level of abstraction during software development. Model-Driven Engineering (MDE) has shown to be a suitable method to raise abstraction levels during software development. Models are primary development artifacts in MDE that describe complex software systems from different viewpoints. In MDE software projects, models are heavily edited through all stages of the development process. During this editing process, the models can become inconsistent due to uncertainties in the software design or various editing mistakes. While most inconsistencies can be tolerated temporarily, they need to be resolved eventually. The resolution of an inconsistency affecting a model’s design is typically a creative process that requires a developer’s expertise. Model repair recommendation tools can guide the developer through this process and propose a ranked list of repairs to resolve the inconsistency. However, such tools will only be accepted in practice if the list of recommendations is plausible and understandable to a developer. Current approaches mainly focus on exhaustive search strategies to generate improved versions of an inconsistent model. Such resolutions might not be understandable to developers, may not reflect the original intentions of an editing process, or just undo former work. Moreover, those tools typically resolve multiple inconsistencies at a time, which might lead to an incomprehensible composition of repair proposals. This thesis proposes a history-based approach for model repair recommendations. The approach focuses on the detection and complementation of incomplete edit steps, which can be located in the editing history of a model. Edit steps are defined by consistency-preserving edit operations (CPEOs), which formally capture complex and error-prone modifications of a specific modeling language. A recognized incomplete edit step can either be undone or extended to a full execution of a CPEO. The final inconsistency resolution depends on the developer’s approval. The proposed recommendation approach is fully implemented and supported by our interactive repair tool called ReVision. The tool also includes configuration support to generate CPEOs by a semi-automated process. The approach is evaluated using histories of real-world models obtained from popular open-source modeling projects hosted in the Eclipse Git repository. Our experimental results confirm our hypothesis that most of the inconsistencies, namely 93.4%, can be resolved by complementing incomplete edits. 92.6% of the generated repair proposals are relevant in the sense that their effect can be observed in the models’ histories. 94.9% of the relevant repair proposals are ranked at the topmost position. Our empirical results show that the presented history-based model recommendation approach allows developers to repair model inconsistencies efficiently and effectively.Software ist in unserer heutigen Gesellschaft ein alltäglicher Begleiter. Diese wird ständig weiterentwickelt und überarbeitet. Model-Driven Engineering (MDE) hat sich als geeignete Methode erwiesen, um bei der Entwicklung komplexer Software von technischen Details zu abstrahieren. Hierbei werden Modelle als primäre Entwicklungsartefakte verwendet, welche ein Softwaresystem aus verschiedenen Sichten beschreiben. Modelle in MDE werden in allen Entwicklungsphasen einer Software fortlaufend überarbeitet. Während der Bearbeitung können die Modelle aufgrund von Unklarheiten im Design oder verschiedenen Bearbeitungsfehlern inkonsistent werden. Auch wenn Inkonsistenzen vorübergehend toleriert werden können, so müssen diese letztlich doch behoben werden. Die Behebung einer Inkonsistenz, welche sich auf das Design eines Modells auswirkt, ist meist ein kreativer Prozess, der das Fachwissen eines Entwicklers erfordert. Empfehlungswerkzeuge können den Entwickler mit Reparaturvorschlägen unterstützen. Damit solche Werkzeuge in der Praxis akzeptiert werden, müssen die Vorschläge plausible und nachvollziehbar sein. Die meisten aktuelle Ansätze verwenden Suchstrategien, welche Reparaturen durch systematisches Ausprobieren generieren. Die so generierten Reparaturen sind für Entwickler häufig schwer nachvollziehbar, da sie vorhergehende Bearbeitungsschritte nicht beachten oder diese einfach rückgängig machen. Darüber hinaus lösen diese Reparaturwerkzeuge in der Regel mehrere Inkonsistenzen gleichzeitig, was zu unverständlichen und umfangreichen Reparaturen führen kann. Diese Arbeit beschreibt einen Ansatz zum Erkennen und Ergänzen unvollständiger Bearbeitungsschritte, basierend auf der Bearbeitungshistorie eines Modells. Dazu werden konsistenzerhaltende Bearbeitungsoperationen definiert, die komplexe und fehleranfällige Änderungen einer bestimmten Modellierungssprache formal erfassen. Ein unvollständiger Bearbeitungsschritt kann dann entweder rückgängig gemacht oder zu einer konsistenzerhaltenden Bearbeitungsoperationen erweitert werden. Die endgültige Reparatur der Inkonsistenz hängt von der Einschätzung des Entwicklers ab. Der vorgeschlagene Ansatz wurde in unserem interaktiven Reparaturwerkzeug ReVision implementiert. Darüber hinaus umfasst das Werkzeug Unterstützung zum Generieren von konsistenzerhaltenden Bearbeitungsoperationen. Das Reparaturverfahren wurde anhand von Historien realer Modelle aus bekannten Open-Source-Modellierungsprojekten im Eclipse-Git-Repository bewertet. Die experimentellen Ergebnisse bestätigen unsere Hypothese, dass die meisten Inkonsistenzen, nämlich 93.4%, durch Ergänzung unvollständiger Bearbeitungen gelöst werden können. 92.6% der generierten Reparaturvorschläge könnten in der entsprechenden Modellhistorie beobachtet werden. Von diesen Reparaturvorschläge wurden 94.9% an erster Stelle vorgeschlagen. Unsere empirischen Ergebnisse zeigen, dass der vorgestellte historienbasierte Modellempfehlungsansatz es Entwicklern ermöglicht, Modellinkonsistenzen effizient und effektiv zu reparieren

Solving polynomial constraints for proving termination of rewriting

A termination problem can be transformed into a set of polynomial constraints. Up to now, several approaches have been studied to deal with these constraints as constraint solving problems. In this thesis, we study in depth some of these approaches, present some advances in each approach.Navarro Marset, RA. (2008). Solving polynomial constraints for proving termination of rewriting. http://hdl.handle.net/10251/13626Archivo delegad

Managing data through the lens of an ontology

Ontology-based data management aims at managing data through the lens of an ontology, that is, a conceptual representation of the domain of interest in the underlying information system. This new paradigm provides several interesting features, many of which have already been proved effective in managing complex information systems. This article introduces the notion of ontology-based data management, illustrating the main ideas underlying the paradigm, and pointing out the importance of knowledge representation and automated reasoning for addressing the technical challenges it introduces