KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

We apply a model checker to the problem of test generation using a new application of mutation analysis. We define syntactic operators, each of which produces a slight variation on a given model. The operators define a form of mutation analysis at the level of the model checker specification. A model checker generates countersamples which distinguish the variations from the original specification. The countersamples can easily be turned into complete test cases, that is, with inputs and expected results. We define two classes of operators: those that produce test cases from which a correct implementation must differ, and those that produce test cases with which it must agree. There are substantial advantages to combining a model checker with mutation analysis. First, test case generation is automatic; each countersample is a complete test case. Second, in sharp contrast to program-based mutation analysis, equivalent mutant identification is also automatic. We apply our method to an example specification and evaluate the resulting test sets with coverage metrics on a Java implementation.

Using Model Checking to Generate Tests from Specifications | NIST

Manufacturers are faced with many challenges, such as globalization, disparate enterprise systems and a lack of platform maturity. These will only multiply as technology advances, offshore manufacturing increases and new ways of working emerge. To overcome these challenges, many manufacturers have embraced product lifecycle management (PLM) or have expanded the use of PLM processes and tools to manage product data effi ciently, with increased visibility and control over the life cycle of the product.

/pdf/product-lifecycle-management-4mlr7de931.pdf

Product lifecycle management

A survey is given of the main issues in compositional reasoning about state-based parallelism and of the history of their evolution, as re ected in the current literature. Compositional proof techniques are presented as the proof-theoretical analogue of Dijkstra's hierarchicallystructured program development. Machine-support for compositional reasoning, and the relationship between compositionality and modularity are discussed. The issues when compositional reasoning about concurrency is successful, and when it isn't, are commented upon. Pointers to the other papers in this volume are provided.

http://www.beck-shop.de/fachbuch/inhaltsverzeichnis/9783540654933_TOC_001.pdf

Compositionality: The Significant Difference

https://www.isis.vanderbilt.edu/sites/default/files/Agrawal_A_3_27_2004_Semantic_T.pdf

Semantic Translation of Simulink/Stateflow Models to Hybrid Automata Using Graph Transformations

In this paper, we propose a new logical approach to represent and to reason about different time granularities. We identify a time granularity as an infinite sequence of time points properly labelled with proposition symbols marking the starting and ending points of the corresponding granules, and we symbolically model sets of granularities by means of linear time logic formulas. Some real-world granularities are provided, from a clinical domain and from the Gregorian Calendar, to motivate and exemplify our approach. Different formulas are introduced, which represent relations between different granularities. The proposed framework permits one to algorithmically solve the consistency, the equivalence, and the classification problems in a uniform way, by reducing them to the validity problem for the considered linear time logic.

/pdf/representing-and-reasoning-about-temporal-granularities-32i4hzaoty.pdf

Representing and Reasoning about Temporal Granularities

Motivation: Identification of differential expressed genes has led to countless new discoveries. However, differentially expressed genes are only a proxy for finding dysregulated pathways. The problem is to identify how the network of regulatory and physical interactions rewires in different conditions or in disease.

Results: We developed a procedure named DINA (DIfferential Network Analysis), which is able to identify set of genes, whose co-regulation is condition-specific, starting from a collection of condition-specific gene expression profiles. DINA is also able to predict which transcription factors (TFs) may be responsible for the pathway condition-specific co-regulation. We derived 30 tissue-specific gene networks in human and identified several metabolic pathways as the most differentially regulated across the tissues. We correctly identified TFs such as Nuclear Receptors as their main regulators and demonstrated that a gene with unknown function (YEATS2) acts as a negative regulator of hepatocyte metabolism. Finally, we showed that DINA can be used to make hypotheses on dysregulated pathways during disease progression. By analyzing gene expression profiles across primary and transformed hepatocytes, DINA identified hepatocarcinoma-specific metabolic and transcriptional pathway dysregulation.

Availability: We implemented an on-line web-tool http://dina.tigem.it enabling the user to apply DINA to identify tissue-specific pathways or gene signatures.

Contact: ti.megit@odranrebid

Supplementary information: Supplementary data are available at Bioinformatics online.

/pdf/differential-network-analysis-for-the-identification-of-1yo7djdrzs.pdf

Differential Network Analysis for the identification of condition-specific pathway activity and regulation

We present a new semantics of Statecharts that excludes failures and a compositional formulation of this semantics based on Labelled Transition Systems (LTS). We consider a hierarchy of LTS equivalences and we study their congruence properties w.r. to statechart operators.

Equivalences of Statecharts

Model checking is a powerful method widely explored in formal verification. Given a model of a system, e.g., a Kripke structure, and a formula specifying its expected behaviour, one can verify whether the system meets the behaviour by checking the formula against the model. Classically, system behaviour is expressed by a formula of a temporal logic, such as LTL and the like. These logics are "point-wise" interpreted, as they describe how the system evolves state-by-state. However, there are relevant properties, such as those constraining the temporal relations between pairs of temporally extended events or involving temporal aggregations, which are inherently "interval-based", and thus asking for an interval temporal logic. In this paper, we give a formalization of the model checking problem in an interval logic setting. First, we provide an interpretation of formulas of Halpern and Shoham's interval temporal logic HS over finite Kripke structures, which allows one to check interval properties of computations. Then, we prove that the model checking problem for HS against finite Kripke structures is decidable by a suitable small model theorem, and we provide a lower bound to its computational complexity.

Checking interval properties of computations

Model checking is a useful method to verify automatically the correctness of a system with respect to a desired behavior, by checking whether a mathematical model of the system satisfies a formal specification of this behavior. Many systems of interest are open, in the sense that their behavior depends on the interaction with their environment. The model checking problem for finite-state open systems (called module checking) has been intensively studied in the literature. In this paper, we focus on open pushdown systems and we study the related model-checking problem (pushdown module checking, for short) with respect to properties expressed by CTL and CTL * formulas. We show that pushdown module checking against CTL (resp., CTL *) is 2Exptime-complete (resp., 3Exptime-complete). Moreover, we prove that for a fixed CTL or CTL * formula, the problem is Exptime-complete.

/pdf/pushdown-module-checking-x8esnj34ye.pdf

Adriano Peron

Papers

Representing and Reasoning about Temporal Granularities

Differential Network Analysis for the identification of condition-specific pathway activity and regulation

Equivalences of Statecharts

Checking interval properties of computations

Pushdown module checking