46 research outputs found

    Automated and foundational verification of low-level programs

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    Formal verification is a promising technique to ensure the reliability of low-level programs like operating systems and hypervisors, since it can show the absence of whole classes of bugs and prevent critical vulnerabilities. However, to realize the full potential of formal verification for real-world low-level programs one has to overcome several challenges, including: (1) dealing with the complexities of realistic models of real-world programming languages; (2) ensuring the trustworthiness of the verification, ideally by providing foundational proofs (i.e., proofs that can be checked by a general-purpose proof assistant); and (3) minimizing the manual effort required for verification by providing a high degree of automation. This dissertation presents multiple projects that advance formal verification along these three axes: RefinedC provides the first approach for verifying C code that combines foundational proofs with a high degree of automation via a novel refinement and ownership type system. Islaris shows how to scale verification of assembly code to realistic models of modern instruction set architectures-in particular, Armv8-A and RISC-V. DimSum develops a decentralized approach for reasoning about programs that consist of components written in multiple different languages (e.g., assembly and C), as is common for low-level programs. RefinedC and Islaris rest on Lithium, a novel proof engine for separation logic that combines automation with foundational proofs.Formale Verifikation ist eine vielversprechende Technik, um die Verlässlichkeit von grundlegenden Programmen wie Betriebssystemen sicherzustellen. Um das volle Potenzial formaler Verifikation zu realisieren, müssen jedoch mehrere Herausforderungen gemeistert werden: Erstens muss die Komplexität von realistischen Modellen von Programmiersprachen wie C oder Assembler gehandhabt werden. Zweitens muss die Vertrauenswürdigkeit der Verifikation sichergestellt werden, idealerweise durch maschinenüberprüfbare Beweise. Drittens muss die Verifikation automatisiert werden, um den manuellen Aufwand zu minimieren. Diese Dissertation präsentiert mehrere Projekte, die formale Verifikation entlang dieser Achsen weiterentwickeln: RefinedC ist der erste Ansatz für die Verifikation von C Code, der maschinenüberprüfbare Beweise mit einem hohen Grad an Automatisierung vereint. Islaris zeigt, wie die Verifikation von Assembler zu realistischen Modellen von modernen Befehlssatzarchitekturen wie Armv8-A oder RISC-V skaliert werden kann. DimSum entwickelt einen neuen Ansatz für die Verifizierung von Programmen, die aus Komponenten in mehreren Programmiersprachen bestehen (z.B., C und Assembler), wie es oft bei grundlegenden Programmen wie Betriebssystemen der Fall ist. RefinedC und Islaris basieren auf Lithium, eine neue Automatisierungstechnik für Separationslogik, die maschinenüberprüfbare Beweise und Automatisierung verbindet.This research was supported in part by a Google PhD Fellowship, in part by awards from Android Security's ASPIRE program and from Google Research, and in part by a European Research Council (ERC) Consolidator Grant for the project "RustBelt", funded under the European Union’s Horizon 2020 Framework Programme (grant agreement no. 683289)

    Towards Porting Operating Systems with Program Synthesis

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    The end of Moore's Law has ushered in a diversity of hardware not seen in decades. Operating system (and system software) portability is accordingly becoming increasingly critical. Simultaneously, there has been tremendous progress in program synthesis. We set out to explore the feasibility of using modern program synthesis to generate the machine-dependent parts of an operating system. Our ultimate goal is to generate new ports automatically from descriptions of new machines. One of the issues involved is writing specifications, both for machine-dependent operating system functionality and for instruction set architectures. We designed two domain-specific languages: Alewife for machine-independent specifications of machine-dependent operating system functionality and Cassiopea for describing instruction set architecture semantics. Automated porting also requires an implementation. We developed a toolchain that, given an Alewife specification and a Cassiopea machine description, specializes the machine-independent specification to the target instruction set architecture and synthesizes an implementation in assembly language with a customized symbolic execution engine. Using this approach, we demonstrate successful synthesis of a total of 140 OS components from two pre-existing OSes for four real hardware platforms. We also developed several optimization methods for OS-related assembly synthesis to improve scalability. The effectiveness of our languages and ability to synthesize code for all 140 specifications is evidence of the feasibility of program synthesis for machine-dependent OS code. However, many research challenges remain; we also discuss the benefits and limitations of our synthesis-based approach to automated OS porting.Comment: ACM Transactions on Programming Languages and Systems. Accepted on August 202

    Selecting fault revealing mutants

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    Mutant selection refers to the problem of choosing, among a large number of mutants, the (few) ones that should be used by the testers. In view of this, we investigate the problem of selecting the fault revealing mutants, i.e., the mutants that are killable and lead to test cases that uncover unknown program faults. We formulate two variants of this problem: the fault revealing mutant selection and the fault revealing mutant prioritization. We argue and show that these problems can be tackled through a set of ‘static’ program features and propose a machine learning approach, named FaRM, that learns to select and rank killable and fault revealing mutants. Experimental results involving 1,692 real faults show the practical benefits of our approach in both examined problems. Our results show that FaRM achieves a good trade-off between application cost and effectiveness (measured in terms of faults revealed). We also show that FaRM outperforms all the existing mutant selection methods, i.e., the random mutant sampling, the selective mutation and defect prediction (mutating the code areas pointed by defect prediction). In particular, our results show that with respect to mutant selection, our approach reveals 23% to 34% more faults than any of the baseline methods, while, with respect to mutant prioritization, it achieves higher average percentage of revealed faults with a median difference between 4% and 9% (from the random mutant orderings)

    Selecting fault revealing mutants

    Get PDF
    Mutant selection refers to the problem of choosing, among a large number of mutants, the (few) ones that should be used by the testers. In view of this, we investigate the problem of selecting the fault revealing mutants, i.e., the mutants that are killable and lead to test cases that uncover unknown program faults. We formulate two variants of this problem: the fault revealing mutant selection and the fault revealing mutant prioritization. We argue and show that these problems can be tackled through a set of ‘static’ program features and propose a machine learning approach, named FaRM, that learns to select and rank killable and fault revealing mutants. Experimental results involving 1,692 real faults show the practical benefits of our approach in both examined problems. Our results show that FaRM achieves a good trade-off between application cost and effectiveness (measured in terms of faults revealed). We also show that FaRM outperforms all the existing mutant selection methods, i.e., the random mutant sampling, the selective mutation and defect prediction (mutating the code areas pointed by defect prediction). In particular, our results show that with respect to mutant selection, our approach reveals 23% to 34% more faults than any of the baseline methods, while, with respect to mutant prioritization, it achieves higher average percentage of revealed faults with a median difference between 4% and 9% (from the random mutant orderings)

    Energy-efficient Transitional Near-* Computing

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    Studies have shown that communication networks, devices accessing the Internet, and data centers account for 4.6% of the worldwide electricity consumption. Although data centers, core network equipment, and mobile devices are getting more energy-efficient, the amount of data that is being processed, transferred, and stored is vastly increasing. Recent computer paradigms, such as fog and edge computing, try to improve this situation by processing data near the user, the network, the devices, and the data itself. In this thesis, these trends are summarized under the new term near-* or near-everything computing. Furthermore, a novel paradigm designed to increase the energy efficiency of near-* computing is proposed: transitional computing. It transfers multi-mechanism transitions, a recently developed paradigm for a highly adaptable future Internet, from the field of communication systems to computing systems. Moreover, three types of novel transitions are introduced to achieve gains in energy efficiency in near-* environments, spanning from private Infrastructure-as-a-Service (IaaS) clouds, Software-defined Wireless Networks (SDWNs) at the edge of the network, Disruption-Tolerant Information-Centric Networks (DTN-ICNs) involving mobile devices, sensors, edge devices as well as programmable components on a mobile System-on-a-Chip (SoC). Finally, the novel idea of transitional near-* computing for emergency response applications is presented to assist rescuers and affected persons during an emergency event or a disaster, although connections to cloud services and social networks might be disturbed by network outages, and network bandwidth and battery power of mobile devices might be limited
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