12th International Workshop on Termination (WST 2012) : WST 2012, February 19–23, 2012, Obergurgl, Austria / ed. by Georg Moser

This volume contains the proceedings of the 12th International Workshop on Termination (WST 2012), to be held February 19–23, 2012 in Obergurgl, Austria. The goal of the Workshop on Termination is to be a venue for presentation and discussion of all topics in and around termination. In this way, the workshop tries to bridge the gaps between different communities interested and active in research in and around termination. The 12th International Workshop on Termination in Obergurgl continues the successful workshops held in St. Andrews (1993), La Bresse (1995), Ede (1997), Dagstuhl (1999), Utrecht (2001), Valencia (2003), Aachen (2004), Seattle (2006), Paris (2007), Leipzig (2009), and Edinburgh (2010). The 12th International Workshop on Termination did welcome contributions on all aspects of termination and complexity analysis. Contributions from the imperative, constraint, functional, and logic programming communities, and papers investigating applications of complexity or termination (for example in program transformation or theorem proving) were particularly welcome. We did receive 18 submissions which all were accepted. Each paper was assigned two reviewers. In addition to these 18 contributed talks, WST 2012, hosts three invited talks by Alexander Krauss, Martin Hofmann, and Fausto Spoto

Negative Emotional Content Disrupts the Coherence of Episodic Memories

Events are thought to be stored in episodic memory as coherent representations, in which the constituent elements are bound together so that a cue can trigger reexperience of all elements via pattern completion. Negative emotional content can strongly influence memory, but opposing theories predict strengthening or weakening of memory coherence. Across a series of experiments, participants imagined a number of person-location-object events with half of the events including a negative element (e.g., an injured person), and memory was tested across all within event associations. We show that the presence of a negative element reduces memory for associations between event elements, including between neutral elements encoded after a negative element. The presence of a negative element reduces the coherence with which a multimodal event is remembered. Our results, supported by a computational model, suggest that coherent retrieval from neutral events is supported by pattern completion, but that negative content weakens associative encoding which impairs this process. Our findings have important implications for understanding the way traumatic events are encoded and support therapeutic strategies aimed at restoring associations between negative content and its surrounding context

Answer Set Programming based on Propositional Satisfiability

Answer set programming (ASP) emerged in the late 1990s as a new logic programming paradigm that has been successfully applied in various application domains. Also motivated by the availability of efficient solvers for propositional satisfiability (SAT), various reductions from logic programs to SAT were introduced. All these reductions, however, are limited to a subclass of logic programs or introduce new variables or may produce exponentially bigger propositional formulas. In this paper, we present a SAT-based procedure, called ASPSAT, that (1) deals with any (nondisjunctive) logic program, (2) works on a propositional formula without additional variables (except for those possibly introduced by the clause form transformation), and (3) is guaranteed to work in polynomial space. From a theoretical perspective, we prove soundness and completeness of ASPSAT. From a practical perspective, we have (1) implemented ASPSAT in Cmodels, (2) extended the basic procedures in order to incorporate the most popular SAT reasoning strategies, and (3) conducted an extensive comparative analysis involving other state-of-the-art answer set solvers. The experimental analysis shows that our solver is competitive with the other solvers we considered and that the reasoning strategies that work best on ‘small but hard’ problems are ineffective on ‘big but easy’ problems and vice versa

Improving software quality with programming patterns

Software systems and services are increasingly important, involving and improving the work and lives of billions people. However, software development is still human-intensive and error-prone. Established studies report that software failures cost the global economy $312 billion annually and software vendors often spend 50-75% of the total development cost for finding and fixing bugs, i.e. subtle programming errors that cause software failures. People rarely develop software from scratch, but frequently reuse existing software artifacts. In this dissertation, we focus on programming patterns, i.e. frequently occurring code resulted from reuse, and explore their potential for improving software quality. Specially, we develop techniques for recovering programming patterns and using them to find, fix, and prevent bugs more effectively. This dissertation has two main contributions. One is Graph-based Object Usage Model (GROUM), a graph-based representation of source code. A GROUM abstracts a fragment of code as a graph representing its object usages. In a GROUM, nodes correspond to the function calls and control structures while edges capture control and data relationships between them. Based on GROUM, we developed a graph mining technique that could recover programming patterns of API usage and use them for detecting bugs. GROUM is also used to find similar bugs and recommend similar bug fixes. The other main contribution of this dissertation is SLAMC, a Statistical Semantic LAnguage Model for Source Code. SLAMC represents code as sequences of code elements of different roles, e.g. data types, variables, or functions and annotate those elements with sememes, a text-based annotation of their semantic information. SLAMC models the regularities over the sememe sequences code-based factors like local code context, global concerns, and pair-wise associations, thus, implicitly captures programming idioms and patterns as sequences with high probabilities. Based on SLAMC, we developed a technique for recommending most likely next code sequences, which could improve programming productivity and might reduce the odds of programming errors. Empirical evaluation shows that our approaches can detect meaningful programming patterns and anomalies that might cause bugs or maintenance issues, thus could improve software quality. In addition, our models have been successfully used for several other problems, from library adaptation, code migration, to bug fix generation. They also have several other potential applications, which we will explore in the future work

Distributed Implementation of SIGNAL: Scheduling & Graph Clustering

International audienceThis paper introduces the scheduling strategy and some key tools which have been designed for the distributed implementation of Signal, a real-time synchronous dataflow language. First, we motivate a scheduling strategy with respect to the reactivity and time-predictability requirements bound to real-time computing. Then, several key tools to implement this scheduling strategy are described. These tools are acting on the concept of Synchronous-Flow Dependence Graph (SFD Graph) which defines a generalization of Directed Acyclic Graph and constitutes the abstract representation of Signal programs. The tools presented in this paper are: (a) the abstraction of SFD graphs which enables grain-size tuning according to the target architecture, (b) the notion of scheduling over SFD graphs and (c) qualitative clustering tools based on the notion of Compositional Deadlock Consistency