The electrum analyzer: Model checking relational first-order temporal specifications

This paper presents the Electrum Analyzer, a free-software tool to validate and perform model checking of Electrum specifications. Electrum is an extension of Alloy that enriches its relational logic with LTL operators, thus simplifying the specification of dynamic systems. The Analyzer supports both automatic bounded model checking, with an encoding into SAT, and unbounded model checking, with an encoding into SMV. Instance, or counter-example, traces are presented back to the user in a unified visualizer. Features to speed up model checking are offered, including a decomposed parallel solving strategy and the extraction of symbolic bounds. Source code: https://github.com/haslab/ElectrumVideo: https://youtu.be/FbjlpvjgMDA.European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalisation (COMPETE2020) and by National Funds through the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT) within project POCI-01-0145-FEDER-016826, and the French Research Agency project FORMEDICIS ANR-16-CE25-000

A First Step in the Translation of Alloy to Coq

International audienceAlloy is both a formal language and a tool for software mod-eling. The language is basically first order relational logic. The analyzer is based on instance finding: it tries to refute assertions and if it succeeds it reports a counterexample. It works by translating Alloy models and instance finding into SAT problems. If no instance is found it does not mean the assertion is satisfied. Alloy relies on the small scope hypothesis: examining all small cases is likely to produce interesting counterexamples. This is very valuable when developing a system. However, Alloy cannot show their absence. In this paper, we propose an approach where Alloy can be used as a first step, and then using a tool we develop, Alloy models can be translated to Coq code to be proved correct interactively

Target oriented relational model finding

Lecture Notes in Computer Science 8411, 2014Model finders are becoming useful in many software engineering problems. Kodkod is one of the most popular, due to its support for relational logic (a combination of first order logic with relational algebra operators and transitive closure), allowing a simpler specification of constraints, and support for partial instances, allowing the specification of a priori (exact, but potentially partial) knowledge about a problem's solution. However, in some software engineering problems, such as model repair or bidirectional model transformation, knowledge about the solution is not exact, but instead there is a known target that the solution should approximate. In this paper we extend Kodkod's partial instances to allow the specification of such targets, and show how its model finding procedure can be adapted to support them (using both PMax-SAT solvers or SAT solvers with cardinality constraints). Two case studies are also presented, including a careful performance evaluation to assess the effectiveness of the proposed extension.(undefined

Synthesizing Iterators from Abstraction Functions

A technique for synthesizing iterators from declarative abstraction functions written in a relational logic specification language is described. The logic includes a transitive closure operator that makes it convenient for expressing reachability queries on linked data structures. Some optimizations, including tuple elimination, iterator flattening, and traversal state reduction, are used to improve performance of the generated iterators. A case study demonstrates that most of the iterators in the widely used JDK Collections classes can be replaced with code synthesized from declarative abstraction functions. These synthesized iterators perform competitively with the hand-written originals. In a user study the synthesized iterators always passed more test cases than the hand-written ones, were almost always as efficient, usually took less programmer effort, and were the qualitative preference of all participants who provided free-form comments

Operationalising learning from rare events: framework for middle humanitarian operations managers

The purpose of this paper is to investigate the learning from rare events and the knowledge management processinvolved, which presents a significant challenge to many organizations. This is primarily attributed to the inability tointerpret these events in a systematic and “rich” manner, which this paper seeks to address. We start by summarizing therelevant literature on humanitarian operations management (HOM), outlining the evolution of the socio-technical disasterlifecycle and its relationship with humanitarian operations, using a supply chain resilience theoretical lens. We then out-line theories of organizational learning (and unlearning) from disasters and the impact on humanitarian operations. Subse-quently, we theorize the role of middle managers in humanitarian operations, which is the main focus of our paper. Themain methodology incorporates a hybrid of two techniques for root cause analysis, applied to two related case studies.The cases were specifically selected as, despite occurring twenty years apart, there are many similarities in the chain ofcausation and supporting factors, potentially suggesting that adequate learning from experience and failures is not occur-ring. This provides a novel learning experience within the HOM paradigm. Hence, the proposed approach is based on amultilevel structure that facilitates the operationalization of learning from rare events in humanitarian operations. Theresults show that we are able to provide an environment for multiple interpretations and effective learning, with emphasison middle managers within a humanitarian operations and crisis/disaster management context