RPP: Automatic Proof of Relational Properties by Self-Composition

Self-composition provides a powerful theoretical approach to prove relational properties, i.e. properties relating several program executions, that has been applied to compare two runs of one or similar programs (in secure dataflow properties, code transformations, etc.). This tool demo paper presents RPP, an original implementation of self-composition for specification and verification of relational properties in C programs in the FRAMA-C platform. We consider a very general notion of relational properties invoking any finite number of function calls of possibly dissimilar functions with possible nested calls. The new tool allows the user to specify a relational property, to prove it in a completely automatic way using classic deductive verification, and to use it as a hypothesis in the proof of other properties that may rely on it

Inferring Interval-Valued Floating-Point Preconditions

Aggregated roundoff errors caused by floating-point arithmetic can make numerical code highly unreliable. Verified postconditions for floating-point functions can guarantee the accuracy of their results under specific preconditions on the function inputs, but how to systematically find an adequate precondition for a desired error bound has not been explored so far. We present two novel techniques for automatically synthesizing preconditions for floating-point functions that guarantee that user-provided accuracy requirements are satisfied. Our evaluation on a standard benchmark set shows that our approaches are complementary and able to find accurate preconditions in reasonable time

Certified Verification of Relational Properties

The use of function contracts to specify the behavior of functions often remains limited to the scope of a single function call. Relational properties link several function calls together within a single specification. They can express more advanced properties of a given function, such as non-interference, continuity, or monotonicity. They can also relate calls to different functions, for instance, to show that an optimized implementation is equivalent to its original counterpart. However, relational properties cannot be expressed and verified directly in the traditional setting of modular deductive verification. Self-composition has been proposed to overcome this limitation, but it requires complex transformations and additional separation hypotheses for real-life languages with pointers. We propose a novel approach that is not based on code transformation and avoids those drawbacks. It directly applies a verification condition generator to produce logical formulas that must be verified to ensure a given relational property. The approach has been fully formalized and proved sound in the Coq proof assistant

The UniProt-GO Annotation database in 2011

The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360 000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data se

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Propriétés relationnnelles pour la spécification et la vérification de programmes C avec Frama-C

Deductive verification techniques provide powerful methods for formal verification of properties expressed in Hoare Logic. In this formalization, also known as axiomatic semantics, a program is seen as a predicate transformer, where each program c executed on a state verifying a property P leads to a state verifying another property Q. Relational properties, on the other hand, link n program to two properties. More precisely, a relational property is a property about n programs c1; :::; cn stating that if each program ci starts in a state si and ends in a state s0 i such that P(s1; :::; sn) holds, then Q(s0 1; :::; s0 n) holds. Thus, relational properties invoke any finite number of executions of possibly dissimilar programs. Such properties cannot be expressed directly in the traditional setting of modular deductive verification, as axiomatic semantics cannot refer to two distinct executions of a program c, or different programs c1 and c2. This thesis brings two solutions to the deductive verification of relational properties. Both of them make it possible to prove a relational property and to use it as a hypothesis in the subsequent verifications. We model our solutions using a small imperative language containing procedure calls. Both solutions are implemented in the context of the C programming language, the FRAMA-C platform, the ACSL specification language and the deductive verification plugin WP. The new tool, called RPP, allows one to specify a relational property, to prove it using classic deductive verification, and to use it as hypothesis in the proof of other properties. The tool is evaluated over a set of illustrative examples. Experiments have also been made on runtime checking of relational properties and counterexample generation when a property cannot be proved.Les techniques de vérification déductive fournissent des méthodes puissantes pour la vérification formelle des propriétés exprimées dans la Logique de Hoare. Dans cette formalisation, également connue sous le nom de sémantique axiomatique, un programme est considéré comme un transformateur de prédicat, où chaque programme c exécuté sur un état vérifiant une propriété P conduit à un état vérifiant une autre propriété Q. Les propriétés relationnelles, de leur côté, lient un ensemble de programmes à deux propriétés. Plus précisément, une propriété relationnelle est une propriété concernant n programmes c1; ::::; cn, indiquant que si chaque programme ci commence dans un état si et termine dans un état s0 i tel que P(s1; ::::; sn) soit vérifié, alors Q(s0 1; :::; s0 n) est vérifié. Ainsi, les propriétés relationnelles invoquent tout nombre fini d’exécutions de programmes éventuellement dissemblables. De telles propriétés ne peuvent pas être exprimées directement dans le cadre traditionnel de la vérification déductive modulaire, car la sémantique axiomatique ne peut se référer à deux exécutions distinctes d’un programme c, ou à des programmes différents c1 et c2. Cette thèse apporte deux solutions à la vérification déductive des propriétés relationnelles. Les deux approches permettent de prouver une propriété relationnelle et de l’utiliser comme hypothèse dans des vérifications ultérieures. Nous modélisons ces solutions à l’aide d’un mini-langage impératif contenant des appels de procédures. Les deux solutions sont implémentées dans le contexte du langage de programmation C, de la plateforme FRAMA-C, du langage de spécification ACSL et du plugin de vérification déductive WP. Le nouvel outil, appelé RPP, permet de spécifier une propriété relationnelle, de la prouver en utilisant la vérification déductive classique, et de l’utiliser comme hypothèse dans la preuve d’autres propriétés. L’outil est évalué sur une série d’exemples illustratifs. Des expériences ont également été faites sur la vérification à l’exécution de propriétés relationnelles et la génération de contre-exemples lorsqu’une propriété ne peut être prouvée

La Malinche : Interprète d'un nouveau monde

Le présent mémoire s'intéresse à la vie et au travail de La Malinche, l'une des interprètes de Cortés durant la Conquête du Mexique. Née dans le sud du Mexique actuel et ayant vécu dans les cultures maya et aztèque, La Malinche a été « offerte » aux conquistadors espagnols. Elle s'avèrera une aide précieuse pour faciliter la communication entre les Européens et les Américains. Ce travail propose un résumé du déroulement historique de la Conquête du Mexique ainsi qu'une biographie de La Malinche. Il examine ensuite les divergences culturelles ayant existé entre les Espagnols et les autochtones américains et la manière dont les interprètes de la Conquête ont pu les surmonter, permettant ainsi aux deux groupes de communiquer. Enfin, ce mémoire s'attache à situer le travail de La Malinche dans l'histoire de l'interprétation, en s'interrogeant sur les éventuels liens qui pourraient être tracés entre l'interprétation d'alors et la pratique actuelle