Overview of Formal Methods in Software Engineering
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Data-Driven Grasp Synthesis - a Survey Jeannette Bohg, Member, IEEE, Antonio Morales, Member, IEEE, Tamim Asfour, Member, IEEE, Danica Kragic Member, IEEE
TRANSACTIONS ON ROBOTICS 1 Data-Driven Grasp Synthesis - A Survey Jeannette Bohg, Member, IEEE, Antonio Morales, Member, IEEE, Tamim Asfour, Member, IEEE, Danica Kragic Member, IEEE specific metric. This process is usually based on some existing Abstract—We review the work on data-driven grasp synthesis grasp experience that can be a heuristic or is generated in and the methodologies for sampling and ranking candidate simulation or on a real robot. Kamon et al. [5] refer to this grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown as the comparative and Shimoga [2] as the knowledge-based objects. This structure allows us to identify common object rep- approach. Here, a grasp is commonly parameterized by [6, 7]: resentations and perceptual processes that facilitate the employed • the grasping point on the object with which the tool center data-driven grasp synthesis technique. In the case of known point (TCP) should be aligned, objects, we concentrate on the approaches that are based on • the approach vector which describes the 3D angle that object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching the robot hand approaches the grasping point with, to a set of previously encountered objects. Finally, for the • the wrist orientation of the robotic hand and approaches dealing with unknown objects, the core part is the • an initial finger configuration extraction of specific features that are indicative of good grasps. Data-driven approaches differ in how the set of grasp candi- Our survey provides an overview of the different methodologies dates is sampled, how the grasp quality is estimated and how and discusses open problems in the area of robot grasping. -
Verification and Formal Methods
Verification and Formal Methods: Practice and Experience J. C. P. Woodcock University of York, UK and P. G. Larsen Engineering College of Aarhus, Denmark and J. C. Bicarregui STFC Rutherford Appleton Laboratory, UK and J. S. Fitzgerald Newcastle University, UK We describe the state of the art in the industrial use of formal verification technology. We report on a new survey of the use of formal methods in industry, and compare it with the most significant surveys carried out over the last 20 years. We review the literature on formal methods, and present a series of industrial projects undetaken over the last two decades. We draw some observations from these surveys and records of experience. Based on this, we discuss the issues surrounding the industrial adoption of formal methods. Finally, we look to the future and describe the development of a Verified Software Repository, part of the worldwide Verified Software Initiative. We introduce the initial projects being used to populate the repository, and describe the challenges they address. Categories and Subject Descriptors: D.2.4 [Software/Program Verification]: Assertion check- ers, Class invariants, Correctness proofs, Formal methods, Model checking, Programming by contract; F.3.1 [Specifying and Verifying and Reasoning about Programs]: Assertions, Invariants, Logics of programs, Mechanical verification, Pre- and post-conditions, Specification techniques; F.4.1 [Mathematical Logic]: Mechanical theorem proving; I.2.2 [Automatic Pro- gramming]: Program verification. Additional Key Words and Phrases: Experimental software engineering, formal methods surveys, Grand Challenges, Verified Software Initiative, Verified Software Repository. 1. INTRODUCTION Formal verification, for both hardware and software, has been a topic of great sig- nificance for at least forty years. -
Formal Methods
SE 5302: Formal Methods Course Instructor: Parasara Sridhar Duggirala, Ph.D. Catalog Description. 3 credits. This course is designed to provide students with an introduction to formal methods as a framework for the specification, design, and verification of software-intensive embedded systems. Topics include automata theory, model checking, theorem proving, and system specification. Examples are driven by cyber-physical systems. The course is addressed to students in engineering who have had at least a year of software or embedded systems design experience. Pre- Requisites: SE 5100 or SE 5101 or SE 5102 and at least one year of software or embedded systems design experience. Course Delivery Method. The course will be offered online, asynchronously, in small recorded modules according to the course schedule and syllabus. Direct and live communication with the instructor will be available each week, according to the class schedule, for discussion, questions, examples, and quizzes. Attendance at live sessions is required, and you must notify the instructor in advance if you cannot attend. A social networking tool called Slack will be used to communicate with students and the instructor between live sessions. Course Objective. This course is designed to provide students with an introduction to formal methods as a framework for the specification, design, and verification of software- intensive embedded systems. Topics include automata theory, model checking, theorem proving, and system specification. Examples are driven by control systems and software systems. Anticipated Student Outcomes. By the end of the course, a student will be able to (1) Gain familiarity with current system design flows in industry used for embedded system design, implementation and verification. -
Experiments in Validating Formal Semantics for C
Experiments in validating formal semantics for C Sandrine Blazy ENSIIE and INRIA Rocquencourt [email protected] Abstract. This paper reports on the design of adequate on-machine formal semantics for a certified C compiler. This compiler is an optimiz- ing compiler, that targets critical embedded software. It is written and formally verified using the Coq proof assistant. The main structure of the compiler is very strongly conditioned by the choice of the languages of the compiler, and also by the kind of semantics of these languages. 1 Introduction C is still widely used in industry, especially for developing embedded software. The main reason is the control by the C programmer of all the resources that are required for program execution (e.g. memory layout, memory allocation) and that affect performance. C programs can therefore be very efficient, but the price to pay is a programming effort. For instance, using C pointer arithmetic may be required in order to compute the address of a memory cell. However, a consequence of this freedom given to the C programmer is the presence of run-time errors such as buffer overflows and dangling pointers. Such errors may be difficult to detect in programs, because of C type casts and more generally because the C type system is unsafe. Many static analysis tools attempt to detect such errors in order to ensure safety properties that may be ensured by more recent languages such as Java. For instance, CCured is a program transformation system that adds memory safety guarantees to C programs by verifying statically that memory errors cannot oc- cur and by inserting run-time checks where static verification is insufficient [1]. -
Formal Specification Methods What Are Formal Methods? Objectives Of
ICS 221 Winter 2001 Formal Specification Methods What Are Formal Methods? ! Use of formal notations … Formal Specification Methods ! first-order logic, state machines, etc. ! … in software system descriptions … ! system models, constraints, specifications, designs, etc. David S. Rosenblum ! … for a broad range of effects … ICS 221 ! correctness, reliability, safety, security, etc. Winter 2001 ! … and varying levels of use ! guidance, documentation, rigor, mechanisms Formal method = specification language + formal reasoning Objectives of Formal Methods Why Use Formal Methods? ! Verification ! Formal methods have the potential to ! “Are we building the system right?” improve both software quality and development productivity ! Formal consistency between specificand (the thing being specified) and specification ! Circumvent problems in traditional practices ! Promote insight and understanding ! Validation ! Enhance early error detection ! “Are we building the right system?” ! Develop safe, reliable, secure software-intensive ! Testing for satisfaction of ultimate customer intent systems ! Documentation ! Facilitate verifiability of implementation ! Enable powerful analyses ! Communication among stakeholders ! simulation, animation, proof, execution, transformation ! Gain competitive advantage Why Choose Not to Use Desirable Properties of Formal Formal Methods? Specifications ! Emerging technology with unclear payoff ! Unambiguous ! Lack of experience and evidence of success ! Exactly one specificand (set) satisfies it ! Lack of automated -
Introduction to Model Checking and Temporal Logic¹
Formal Verification Lecture 1: Introduction to Model Checking and Temporal Logic¹ Jacques Fleuriot [email protected] ¹Acknowledgement: Adapted from original material by Paul Jackson, including some additions by Bob Atkey. I Describe formally a specification that we desire the model to satisfy I Check the model satisfies the specification I theorem proving (usually interactive but not necessarily) I Model checking Formal Verification (in a nutshell) I Create a formal model of some system of interest I Hardware I Communication protocol I Software, esp. concurrent software I Check the model satisfies the specification I theorem proving (usually interactive but not necessarily) I Model checking Formal Verification (in a nutshell) I Create a formal model of some system of interest I Hardware I Communication protocol I Software, esp. concurrent software I Describe formally a specification that we desire the model to satisfy Formal Verification (in a nutshell) I Create a formal model of some system of interest I Hardware I Communication protocol I Software, esp. concurrent software I Describe formally a specification that we desire the model to satisfy I Check the model satisfies the specification I theorem proving (usually interactive but not necessarily) I Model checking Introduction to Model Checking I Specifications as Formulas, Programs as Models I Programs are abstracted as Finite State Machines I Formulas are in Temporal Logic 1. For a fixed ϕ, is M j= ϕ true for all M? I Validity of ϕ I This can be done via proof in a theorem prover e.g. Isabelle. 2. For a fixed ϕ, is M j= ϕ true for some M? I Satisfiability 3. -
Certification of a Tool Chain for Deductive Program Verification Paolo Herms
Certification of a Tool Chain for Deductive Program Verification Paolo Herms To cite this version: Paolo Herms. Certification of a Tool Chain for Deductive Program Verification. Other [cs.OH]. Université Paris Sud - Paris XI, 2013. English. NNT : 2013PA112006. tel-00789543 HAL Id: tel-00789543 https://tel.archives-ouvertes.fr/tel-00789543 Submitted on 18 Feb 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. UNIVERSITÉ DE PARIS-SUD École doctorale d’Informatique THÈSE présentée pour obtenir le Grade de Docteur en Sciences de l’Université Paris-Sud Discipline : Informatique PAR Paolo HERMS −! − SUJET : Certification of a Tool Chain for Deductive Program Verification soutenue le 14 janvier 2013 devant la commission d’examen MM. Roberto Di Cosmo Président du Jury Xavier Leroy Rapporteur Gilles Barthe Rapporteur Emmanuel Ledinot Examinateur Burkhart Wolff Examinateur Claude Marché Directeur de Thèse Benjamin Monate Co-directeur de Thèse Jean-François Monin Invité Résumé Cette thèse s’inscrit dans le domaine de la vérification du logiciel. Le but de la vérification du logiciel est d’assurer qu’une implémentation, un programme, répond aux exigences, satis- fait sa spécification. Cela est particulièrement important pour le logiciel critique, tel que des systèmes de contrôle d’avions, trains ou centrales électriques, où un mauvais fonctionnement pendant l’opération aurait des conséquences catastrophiques. -
PROGRAM VERIFICATION Robert S. Boyer and J
PROGRAM VERIFICATION Robert S. Boyer and J Strother Moore To Appear in the Journal of Automated Reasoning The research reported here was supported by National Science Foundation Grant MCS-8202943 and Office of Naval Research Contract N00014-81-K-0634. Institute for Computing Science and Computer Applications The University of Texas at Austin Austin, Texas 78712 1 Computer programs may be regarded as formal mathematical objects whose properties are subject to mathematical proof. Program verification is the use of formal, mathematical techniques to debug software and software specifications. 1. Code Verification How are the properties of computer programs proved? We discuss three approaches in this article: inductive invariants, functional semantics, and explicit semantics. Because the first approach has received by far the most attention, it has produced the most impressive results to date. However, the field is now moving away from the inductive invariant approach. 1.1. Inductive Assertions The so-called Floyd-Hoare inductive assertion method of program verification [25, 33] has its roots in the classic Goldstine and von Neumann reports [53] and handles the usual kind of programming language, of which FORTRAN is perhaps the best example. In this style of verification, the specifier "annotates" certain points in the program with mathematical assertions that are supposed to describe relations that hold between the program variables and the initial input values each time "control" reaches the annotated point. Among these assertions are some that characterize acceptable input and the desired output. By exploring all possible paths from one assertion to the next and analyzing the effects of intervening program statements it is possible to reduce the correctness of the program to the problem of proving certain derived formulas called verification conditions. -
Formal Verification, Model Checking
Introduction Modeling Specification Algorithms Conclusions Formal Verification, Model Checking Radek Pel´anek Introduction Modeling Specification Algorithms Conclusions Motivation Formal Methods: Motivation examples of what can go wrong { first lecture non-intuitiveness of concurrency (particularly with shared resources) mutual exclusion adding puzzle Introduction Modeling Specification Algorithms Conclusions Motivation Formal Methods Formal Methods `Formal Methods' refers to mathematically rigorous techniques and tools for specification design verification of software and hardware systems. Introduction Modeling Specification Algorithms Conclusions Motivation Formal Verification Formal Verification Formal verification is the act of proving or disproving the correctness of a system with respect to a certain formal specification or property. Introduction Modeling Specification Algorithms Conclusions Motivation Formal Verification vs Testing formal verification testing finding bugs medium good proving correctness good - cost high small Introduction Modeling Specification Algorithms Conclusions Motivation Types of Bugs likely rare harmless testing not important catastrophic testing, FVFV Introduction Modeling Specification Algorithms Conclusions Motivation Formal Verification Techniques manual human tries to produce a proof of correctness semi-automatic theorem proving automatic algorithm takes a model (program) and a property; decides whether the model satisfies the property We focus on automatic techniques. Introduction Modeling Specification Algorithms Conclusions -
Implementing a Transformation from BPMN to CSP+T with ATL: Lessons Learnt
Implementing a Transformation from BPMN to CSP+T with ATL: Lessons Learnt Aleksander González1, Luis E. Mendoza1, Manuel I. Capel2 and María A. Pérez1 1 Processes and Systems Department, Simón Bolivar University PO Box 89000, Caracas, 1080-A, Venezuela 2 Software Engineering Department, University of Granada Aynadamar Campus, 18071, Granada, Spain Abstract. Among the challenges to face in order to promote the use of tech- niques of formal verification in organizational environments, there is the possi- bility of offering the integration of features provided by a Model Transforma- tion Language (MTL) as part of a tool very used by business analysts, and from which formal specifications of a model can be generated. This article presents the use of MTL ATLAS Transformation Language (ATL) as a transformation artefact within the domains of Business Process Modelling Notation (BPMN) and Communicating Sequential Processes + Time (CSP+T). It discusses the main difficulties encountered and the lessons learnt when building BTRANSFORMER; a tool developed for the Eclipse platform, which allows us to generate a formal specification in the CSP+T notation from a business process model designed with BPMN. This learning is valid for those who are interested in formalizing a Business Process Modelling Language (BPML) by means of a process calculus or another formal notation. 1 Introduction Business Processes (BP) must be properly and formally specified in order to be able to verify properties, such as scope, structure, performance, capacity, structural consis- tency and concurrency, i.e., those properties of BP which can provide support to the critical success factors of any organization. Formal specification languages and proc- ess algebras, which allow for the exhaustive verification of BP behaviour [17], are used to carry out the formalization of models obtained from Business Process Model- ling (BPM). -
Formal Verification of Diagnosability Via Symbolic Model Checking
Formal Verification of Diagnosability via Symbolic Model Checking Alessandro Cimatti Charles Pecheur Roberto Cavada ITC-irst RIACS/NASA Ames Research Center ITC-irst Povo, Trento, Italy Moffett Field, CA, U.S.A. Povo, Trento, Italy mailto:[email protected] [email protected] [email protected] Abstract observed system. We propose a new, practical approach to the verification of diagnosability, making the following contribu• This paper addresses the formal verification of di• tions. First, we provide a formal characterization of diagnos• agnosis systems. We tackle the problem of diagnos• ability problem, using the idea of context, that explicitly takes ability: given a partially observable dynamic sys• into account the run-time conditions under which it should be tem, and a diagnosis system observing its evolution possible to acquire certain information. over time, we discuss how to verify (at design time) Second, we show that a diagnosability condition for a given if the diagnosis system will be able to infer (at run• plant is violated if and only if a critical pair can be found. A time) the required information on the hidden part of critical pair is a pair of executions that are indistinguishable the dynamic state. We tackle the problem by look• (i.e. share the same inputs and outputs), but hide conditions ing for pairs of scenarios that are observationally that should be distinguished (for instance, to prevent simple indistinguishable, but lead to situations that are re• failures to stay undetected and degenerate into catastrophic quired to be distinguished. We reduce the problem events). We define the coupled twin model of the plant, and to a model checking problem. -
Identifying and Defining Relationships: Techniques for Improving Student Systemic Thinking
AC 2011-897: IDENTIFYING AND DEFINING RELATIONSHIPS: TECH- NIQUES FOR IMPROVING STUDENT SYSTEMIC THINKING Cecelia M. Wigal, University of Tennessee, Chattanooga Cecelia M. Wigal received her Ph.D. in 1998 from Northwestern University and is presently a Professor of Engineering and Assistant Dean of the College of Engineering and Computer Science at the University of Tennessee at Chattanooga (UTC). Her primary areas of interest and expertise include complex process and system analysis, process improvement analysis, and information system analysis with respect to usability and effectiveness. Dr. Wigal is also interested in engineering education reform to address present and future student and national and international needs. c American Society for Engineering Education, 2011 Identifying and Defining Relationships: Techniques for Improving Student Systemic Thinking Abstract ABET, Inc. is looking for graduating undergraduate engineering students who are systems thinkers. However, genuine systems thinking is contrary to the traditional practice of using linear thinking to help solve design problems often used by students and many practitioners. Linear thinking has a tendency to compartmentalize solution options and minimize recognition of relationships between solutions and their elements. Systems thinking, however, has the ability to define the whole system, including its environment, objectives, and parts (subsystems), both static and dynamic, by their relationships. The work discussed here describes two means of introducing freshman engineering students to thinking systemically or holistically when understanding and defining problems. Specifically, the modeling techniques of Rich Pictures and an instructor generated modified IDEF0 model are discussed. These techniques have roles in many applications. In this case they are discussed in regards to their application to the design process.