Towards declarative diagnosis of constraint programs over finite domains

The paper proposes a theoretical approach of the debugging of constraint programs based on a notion of explanation tree. The proposed approach is an attempt to adapt algorithmic debugging to constraint programming. In this theoretical framework for domain reduction, explanations are proof trees explaining value removals. These proof trees are defined by inductive definitions which express the removals of values as consequences of other value removals. Explanations may be considered as the essence of constraint programming. They are a declarative view of the computation trace. The diagnosis consists in locating an error in an explanation rooted by a symptom.Comment: In M. Ronsse, K. De Bosschere (eds), proceedings of the Fifth International Workshop on Automated Debugging (AADEBUG 2003), September 2003, Ghent. cs.SE/030902

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

Research on knowledge representation, machine learning, and knowledge acquisition

Research in knowledge representation, machine learning, and knowledge acquisition performed at Knowledge Systems Lab. is summarized. The major goal of the research was to develop flexible, effective methods for representing the qualitative knowledge necessary for solving large problems that require symbolic reasoning as well as numerical computation. The research focused on integrating different representation methods to describe different kinds of knowledge more effectively than any one method can alone. In particular, emphasis was placed on representing and using spatial information about three dimensional objects and constraints on the arrangement of these objects in space. Another major theme is the development of robust machine learning programs that can be integrated with a variety of intelligent systems. To achieve this goal, learning methods were designed, implemented and experimented within several different problem solving environments

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

Explanations and Proof Trees

This paper proposes a model for explanations in a set theoretical framework using the notions of closure or fixpoint. In this approach, sets of rules associated with monotonic operators allow to define proof trees. The proof trees may be considered as a declarative view of the trace of a computation. We claim they are explanations of the results of a computation. This notion of explanation is applied to constraint logic programming, and it is used for declarative error diagnosis. It is also applied to constraint programming, and used for constraint retraction

Spectrum-Based Fault Localization in Model Transformations

Model transformations play a cornerstone role in Model-Driven Engineering (MDE), as they provide the essential mechanisms for manipulating and transforming models. The correctness of software built using MDE techniques greatly relies on the correctness of model transformations. However, it is challenging and error prone to debug them, and the situation gets more critical as the size and complexity of model transformations grow, where manual debugging is no longer possible. Spectrum-Based Fault Localization (SBFL) uses the results of test cases and their corresponding code coverage information to estimate the likelihood of each program component (e.g., statements) of being faulty. In this article we present an approach to apply SBFL for locating the faulty rules in model transformations. We evaluate the feasibility and accuracy of the approach by comparing the effectiveness of 18 different stateof- the-art SBFL techniques at locating faults in model transformations. Evaluation results revealed that the best techniques, namely Kulcynski2, Mountford, Ochiai, and Zoltar, lead the debugger to inspect a maximum of three rules to locate the bug in around 74% of the cases. Furthermore, we compare our approach with a static approach for fault localization in model transformations, observing a clear superiority of the proposed SBFL-based method.Comisión Interministerial de Ciencia y Tecnología TIN2015-70560-RJunta de Andalucía P12-TIC-186