Formal Verification of Large Software Systems

We introduce a scalable proof structure to facilitate formal verification of large software systems. In our approach, we mechanically synthesize an abstract specification from the software implementation, match its static operational structure to that of the original specification, and organize the proof as the conjunction of a series of lemmas about the specification structure. By setting up a different lemma for each distinct element and proving each lemma independently, we obtain the important benefit that the proof scales easily for large systems. We present details of the approach and an illustration of its application on a challenge problem from the security domai

Collaborative Verification-Driven Engineering of Hybrid Systems

Hybrid systems with both discrete and continuous dynamics are an important model for real-world cyber-physical systems. The key challenge is to ensure their correct functioning w.r.t. safety requirements. Promising techniques to ensure safety seem to be model-driven engineering to develop hybrid systems in a well-defined and traceable manner, and formal verification to prove their correctness. Their combination forms the vision of verification-driven engineering. Often, hybrid systems are rather complex in that they require expertise from many domains (e.g., robotics, control systems, computer science, software engineering, and mechanical engineering). Moreover, despite the remarkable progress in automating formal verification of hybrid systems, the construction of proofs of complex systems often requires nontrivial human guidance, since hybrid systems verification tools solve undecidable problems. It is, thus, not uncommon for development and verification teams to consist of many players with diverse expertise. This paper introduces a verification-driven engineering toolset that extends our previous work on hybrid and arithmetic verification with tools for (i) graphical (UML) and textual modeling of hybrid systems, (ii) exchanging and comparing models and proofs, and (iii) managing verification tasks. This toolset makes it easier to tackle large-scale verification tasks

Incremental bounded model checking for embedded software

Program analysis is on the brink of mainstream usage in embedded systems development. Formal verification of behavioural requirements, finding runtime errors and test case generation are some of the most common applications of automated verification tools based on bounded model checking (BMC). Existing industrial tools for embedded software use an off-the-shelf bounded model checker and apply it iteratively to verify the program with an increasing number of unwindings. This approach unnecessarily wastes time repeating work that has already been done and fails to exploit the power of incremental SAT solving. This article reports on the extension of the software model checker CBMC to support incremental BMC and its successful integration with the industrial embedded software verification tool BTC EMBEDDED TESTER. We present an extensive evaluation over large industrial embedded programs, mainly from the automotive industry. We show that incremental BMC cuts runtimes by one order of magnitude in comparison to the standard non-incremental approach, enabling the application of formal verification to large and complex embedded software. We furthermore report promising results on analysing programs with arbitrary loop structure using incremental BMC, demonstrating its applicability and potential to verify general software beyond the embedded domain

A Comparative Analysis Of Conventional Software Development Approaches Vs. Formal Methods In Call Distribution Systems

When we think about formal method; the first thing which comes in our mind is mathematical approach. The process of formalization is an approach based on mathematics and used to elaborate the properties of systems (hardware and software). The mathematical modeling or formal methods provide us a framework for large and complex systems. Thus these systems can be specified, analyzed, designed, and verified in a systematic way rather than the approaches which are used conventionally. Formal verification and the methods are applied using theoretical computer science fundamentals to solve the complex and difficult problems in large and complex software and hardware systems to ensure the systems will not fail with run-time errors. Conventional approaches of software verification in call distribution systems rely on quality assurance to verify the system behavior and robustness. The process of software testing cannot show the absence of errors it can only show the presence of errors in software systems. [1] In contrast, the mathematically-based techniques of verification are based on formal methods to prove certain software attributes, for example proving that software does or does not contain the occurrence of errors at run-time such as overflows, divide-by-zero, and access violation, invalid memory access and stack/heap corruption. [1] In this paper later we will have comparative analysis of formal methods vs. conventional software development approaches in call distribution systems. Using this comparison we‘ll try to identify the methodologies and approaches which would be better in SDLC for call distribution systems.

An Abstract Formal Basis for Digital Crowds

Crowdsourcing, together with its related approaches, has become very popular in recent years. All crowdsourcing processes involve the participation of a digital crowd, a large number of people that access a single Internet platform or shared service. In this paper we explore the possibility of applying formal methods, typically used for the verification of software and hardware systems, in analysing the behaviour of a digital crowd. More precisely, we provide a formal description language for specifying digital crowds. We represent digital crowds in which the agents do not directly communicate with each other. We further show how this specification can provide the basis for sophisticated formal methods, in particular formal verification.Comment: 32 pages, 4 figure

Lessons from Formally Verified Deployed Software Systems (Extended version)

The technology of formal software verification has made spectacular advances, but how much does it actually benefit the development of practical software? Considerable disagreement remains about the practicality of building systems with mechanically-checked proofs of correctness. Is this prospect confined to a few expensive, life-critical projects, or can the idea be applied to a wide segment of the software industry? To help answer this question, the present survey examines a range of projects, in various application areas, that have produced formally verified systems and deployed them for actual use. It considers the technologies used, the form of verification applied, the results obtained, and the lessons that can be drawn for the software industry at large and its ability to benefit from formal verification techniques and tools. Note: a short version of this paper is also available, covering in detail only a subset of the considered systems. The present version is intended for full reference.Comment: arXiv admin note: text overlap with arXiv:1211.6186 by other author

A Review of Formal Methods applied to Machine Learning

We review state-of-the-art formal methods applied to the emerging field of the verification of machine learning systems. Formal methods can provide rigorous correctness guarantees on hardware and software systems. Thanks to the availability of mature tools, their use is well established in the industry, and in particular to check safety-critical applications as they undergo a stringent certification process. As machine learning is becoming more popular, machine-learned components are now considered for inclusion in critical systems. This raises the question of their safety and their verification. Yet, established formal methods are limited to classic, i.e. non machine-learned software. Applying formal methods to verify systems that include machine learning has only been considered recently and poses novel challenges in soundness, precision, and scalability. We first recall established formal methods and their current use in an exemplar safety-critical field, avionic software, with a focus on abstract interpretation based techniques as they provide a high level of scalability. This provides a golden standard and sets high expectations for machine learning verification. We then provide a comprehensive and detailed review of the formal methods developed so far for machine learning, highlighting their strengths and limitations. The large majority of them verify trained neural networks and employ either SMT, optimization, or abstract interpretation techniques. We also discuss methods for support vector machines and decision tree ensembles, as well as methods targeting training and data preparation, which are critical but often neglected aspects of machine learning. Finally, we offer perspectives for future research directions towards the formal verification of machine learning systems

thematic series on verification and composition for the internet of services and things

Abstract ■■■ The Internet of Services and Things is characterized as a distributed computing environment that will be populated by a large number of software services and things. Within this context, software systems will increasingly be built by reusing and composing together software services and things distributed over the Internet. This calls for new integration paradigms and patterns, formal composition theories, integration architectures, as well as flexible and dynamic composition and verification mechanisms. In particular, service- and thing-based systems pose new challenges for software composition and verification techniques, due to changing requirements, emerging behaviors, uncertainty, and dynamicity