Specification and inference of trace refinement relations

Chaos Many-Body Engine v03: A new version of code C# for chaos analysis of relativistic many-body systems with reactions

Single particle calculations for a Woods–Saxon potential with triaxial deformations, and large Cartesian oscillator basis (TRIAXIAL 2014, Third version of the code Triaxial)

Confidentiality-preserving refinement describes a relation between a specification and an implementation that ensures that all confidentiality properties required in the specification are preserved by the implementation in a probabilistic setting. The present paper investigates the condition under which that notion of refinement is compositional, i.e. the condition under which refining a subsystem of a larger system yields a confidentiality-preserving refinement of the larger system. It turns out that the refinement relation is not composition in general, but the condition for compositionality can be stated in a way that builds on the analysis of subsystems thus aiding system designers in analyzing a composition.

SCELib3.0: The new revision of SCELib, the parallel computational library of molecular properties in the Single Center Approach

Large-scale high-performance computing systems pose a tough obstacle for today's program analysis tools. Their demands in computational performance and memory capacity for processing program analysis data exceed the capabilities of standard workstations and traditional analysis tools. A comparison of the sophisticated approaches of Vampir NG (VNG), the Debugging Wizard DeWiz and the correctness-checking tool MARMOT provides novel ideas for scalable parallel program analysis. While VNG exploits the power of cluster architectures for near real-time performance analysis, DeWiz utilises distributed computing infrastructures for distinct analysis activities. MARMOT combines automatic runtime and partially distributed analysis.

Modern program analysis and model-based tools are increasingly complex and multi-faceted software systems. They analyze models and programs using advanced type systems, model checking or model finding, abstract interpretation, symbolic verification or a combination thereof. In this talk I will discuss and compare 10 program analysis tools, which MSR build during the last 10 years. They include theorem provers, program verifiers, bug finders, malware scanners, and test case generators. I will describe the need for their development, their innovation, and application. These tools had both had considerable impact on the research community, as well as being shipped in Microsoft products such as the Static Driver Verifier or as part of Visual Studio and other, widely-used internal software development tools. I highlight that many of these analyzers build on generic infrastructure, most of which is available outside of Microsoft as well. With every analyzer build there is a new opportunity, and with every solution there is a new challenge problem. Thus, I will conclude with 10 challenges in program analysis which hopefully triggers new aspiring directions in our joint quest of delivering predictable software that is free from defect and vulnerabilities.KeywordsModel CheckProgram AnalysisChallenge ProblemFormal LanguageConsiderable ImpactThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Various tools for program analysis, including run-time assertion checkers and static analyzers such as verification and test generation tools, require formal specifications of the programs being analyzed. Moreover, many of these tools and techniques require such specifications to be written in a particular style, or follow certain patterns, in order to obtain an acceptable performance from the corresponding analyses. Thus, having a formal specification sometimes is not enough for using a particular technique, since such specification may not be provided in the right formalism. In this paper, we deal with this problem in the increasingly common case of having an operational specification, while for analysis reasons requiring a declarative specification. We propose an evolutionary approach to translate an operational specification written in a sequential programming language, into a declarative specification, in relational logic. We perform experiments on a benchmark of data structure implementations, that show that translating representation invariants using our approach and verifying invariant preservation using the resulting specifications outperforms verification with specifications obtained using an existing semantics-preserving translation. Also, our evolutionary computation translation achieves very good precision in this context.

Solution of the Skyrme–HF+BCS equation on a 3D mesh, II: A new version of the Ev8 code

Satisfiability Modulo Theories (SMT) solvers are used in many modern program verification, analysis and testing tools. They owe their scale and efficiency thanks to advances in search algorithms underlying modern SAT solvers and first-order theorem provers. They owe their versatility in software development applications thanks to specialized algorithms supporting theories, such as numbers and algebraic data-types, of relevance for software engineering. This lecture introduces algorithmic principles of SMT solving, taking as basis modern SAT solvers and integration with specialized theory solvers and quantifier reasoning. We detail some of the algorithms used for main theories used in current SMT solvers and survey newer theories and approaches to integrating solvers. The lectures also outline some application scenarios where SMT solvers have found use, including program verification, network analysis, symbolic model checking, test-case generation, and white-box fuzzing.

The Radiological Safety Analysis Computer (RSAC) Program Version 7.0 (RSAC-7) is the newest version of the RSAC legacy code. It calculates the consequences of a release of radionuclides to the atmosphere. A user can generate a fission product inventory from either reactor operating history or a nuclear criticality event. RSAC-7 models the effects of high-efficiency particulate air filters or other cleanup systems and calculates the decay and ingrowth during transport through processes, facilities, and the environment. Doses are calculated for inhalation, air immersion, ground surface, ingestion, and cloud gamma pathways. RSAC-7 can be used as a tool to evaluate accident conditions in emergency response scenarios, radiological sabotage events and to evaluate safety basis accident consequences. This users’ manual contains the mathematical models and operating instructions for RSAC-7. Instructions, screens, and examples are provided to guide the user through the functions provided by RSAC-7. This program was designed for users who are familiar with radiological dose assessment methods.

Background and Objectives The problem of mental illness-related stigma within healthcare is an area of increasing attention and concern. Understanding Stigma is an anti-stigma workshop for healthcare providers that uses social contact as a core teaching element, along with educational and action-oriented components. The objective of our study was to determine the impact of this program on healthcare providers’ attitudes and behavioural intentions towards patients with a mental illness, and also to ascertain whether various participant and program characteristics affected program outcomes. Our paper reports the results of a pooled analysis from multiple replications of this program in different Canadian jurisdictions between 2013 and 2015. Material and Methods We undertook a pooled analysis of six separate replications of the Understanding Stigma program. All program replications were evaluated using a non-randomized quasi experimental pre- post- follow-up design. The Opening Minds Scale for Health Providers (OMS-HC) was used as the main assessment tool. Study-level and individual-level meta-analysis methods were used to synthesize the data. First, the ‘metan’ command was used to show outcomes by study, using a forest plot. Then, a pooled dataset was produced and analyzed using a random intercept linear mixed model approach with each program being modelled as a random effect. Program and participant characteristics were examined as independent variables using this approach. These were each entered individually. Individual tests included pre to post change by program version (original or condensed), by occupation (nurses versus other healthcare providers), by gender, age, and previous diagnosis of a mental illness. Results Program effect sizes ranged from .19 to .51 (Cohen’s d), with an overall combined effect size of .30. The results of the mixed model analysis showed the improvement from pre to post intervention was statistically significant for the total scale and subscales. Analysis of program and participant factors found that version type, healthcare provider type, gender, and previous diagnosis of a mental illness were all non-significant factors on program outcomes. A significant inverse association was revealed between increasing age and score change. Results also showed a significant positive linear relationship between baseline score and improvement from pre to post intervention. Maintenance of scores at follow-up was observed for participants who attended a booster session. Conclusions The results are promising for the effectiveness of this brief intervention model for reducing stigmatizing attitudes and improving behavioural intentions among nurses and other healthcare providers.

Interactive program analysis tools are often tailored to one particular representation of programs, making adaptation to a new language costly. One way to ease adaptability is to introduce an intermediate abstraction—an adaptation layer—between an existing language representation and the program analysis tool. This adaptation layer translates the tool's queries into queries on the particular representation.Our experiments with this approach on the StarTool program analysis tool resulted in low-cost retargets for C, Tcl/Tk, and Ada. Required adjustments to the approach, however, led to insights for improving a client's retargetability. First, retargeting was eased by having our tool import a tool-centric (i.e., client-centric) interface rather than a general-purpose, language-neutral representation interface. Second, our adaptation layer exports two interfaces, a representation interface supporting queries on the represented program and a language interface that the client queries to configure itself suitably for the given language. Straightforward object-oriented extensions enhance reuse and ease the development of multi-language tools.

We present a notion of refinement between agent-oriented systems defined using alternating-time temporal logic (ATL). The refinement relation provides a framework for defining roles in a society of interacting agents, and formalising a relation of conformance between agents and roles. The refinement relation also allows us to construct abstractions in order to make verification more tractable.

MSTor 2023: A new version of the computer code for multistructural torsional anharmonicity, now with automatic torsional identification using redundant internal coordinates

This paper describesMicroScope, a framework for developing analysis tools for Lisp programs. MicroScope uses a knowledge-intensive approach for program representation and analysis. The analysis tools share a common object oriented program database, and a common Prolog inference engine. The use of Prolog and a declarative representation for programs permits sharing of information, and provides high bandwidth communication between diverse analysis tools. It also supports program specification and debugging activities in the same framework. Extensions to Prolog to support analysis are described, and two tools, theCritic and theExpector, are presented.