Variability-aware analysis is critical for ensuring the quality of configurable C software. An important step toward the development of variability-aware analysis at scale is to transform real-world C software that uses both C and preprocessor into pure C code, by replacing the preprocessor's compile-time variability with C's runtime-variability. In this work, we design and implement a desugaring tool, SugarC, that transforms away real-world preprocessor usage. SugarC augments C's formal grammar specification with translation rules, performs simultaneous type checking during desugaring, and introduces numerous optimizations to address challenges that appear in real-world preprocessor usage. The experiments on DesugarBench, a benchmark consisting of 108 manually-created programs, show that SugarC supports many more language features than two existing desugaring tools. When applied on three real-world configurable C software, SugarC desugared 774 out of 813 files in the three programs, taking at most ten minutes in the worst case and less than two minutes for 95% of the C files.
Leveraging Textual Specifications for Grammar-based Fuzzing of Network Protocols.
Grammar-based fuzzing is a technique used to find soft- ware vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics. Most grammar-based fuzzers for network protocols rely on human experts to manually specify these rules. In this work we study automated learning of protocol rules from textual specifications (i.e. RFCs). We evaluate the automatically extracted protocol rules by applying them to a state-of-the-art fuzzer for transport protocols and show that it leads to a smaller number of test cases while finding the same attacks as the system that uses manually specified rules.
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- Proceedings of the ... Innovative Applications of Artificial Intelligence Conference
- Page Range or eLocation-ID:
- Sponsoring Org:
- National Science Foundation
More Like this
Designing and implementing distributed systems correctly is a very challenging task. Recently, formal verification has been successfully used to prove the correctness of distributed systems. At the heart of formal verification lies a computer-checked proof with an inductive invariant. Finding this inductive invariant, however, is the most difficult part of the proof. Alas, current proof techniques require inductive invariants to be found manually—and painstakingly—by the developer. In this paper, we present a new approach, Incremental Inference of Inductive Invariants (I4), to automatically generate inductive invariants for distributed protocols. The essence of our idea is simple: the inductive invariant of a finite instance of the protocol can be used to infer a general inductive invariant for the infinite distributed protocol. In I4, we create a finite instance of the protocol; use a model checking tool to automatically derive the inductive invariant for this finite instance; and generalize this invariant to an inductive invariant for the infinite protocol. Our experiments show that I4 can prove the correctness of several distributed protocols like Chord, 2PC and Transaction Chains with little to no human effort.
Distributed systems are notoriously difficult to design and implement correctly. Formal verification provides correctness proofs, and has recently been successfully applied to various distributed systems. At the heart of a typical formal verification is a computer-checked proof with an inductive invariant. Finding this inductive invariant is the hardest part of the proof: a part that is currently undertaken manually by the developer and is responsible for most of the effort associated with formal verification. In this paper, we present a new approach: Incremental Inference of Inductive Invariants (I4), to automatically generate inductive invariants for distributed protocols. We start from a simple idea: the inductive invariant of a finite instance of the protocol must be an instance of a general inductive invariant for the infinite distributed protocol. In I4, we instantiate a finite instance of the protocol, work out the finite inductive invariant of this instance, then figure out the general inductive invariant as a generalization of the finite invariant. Our experiments show that I4 can finish the general proof of correctness of several systems with minimal human effort.
Fuzz testing has been gaining ground recently with substantial efforts devoted to the area. Typically, fuzzers take a set of seed inputs and leverage random mutations to continually improve the inputs with respect to a cost, e.g. program code coverage, to discover vulnerabilities or bugs. Following this methodology, fuzzers are very good at generating unstructured inputs that achieve high coverage. However fuzzers are less effective when the inputs are structured, say they conform to an input grammar. Due to the nature of random mutations, the overwhelming abundance of inputs generated by this common fuzzing practice often adversely hinders the effectiveness and efficiency of fuzzers on grammar-aware applications. The problem of testing becomes even harder, when the goal is not only to achieve increased code coverage, but also to nd complex vulnerabilities related to other cost measures, say high resource consumption in an application. We propose Saffron an adaptive grammar-based fuzzing approach to effectively and efficiently generate inputs that expose expensive executions in programs. Saffron takes as input a user-provided grammar, which describes the input space of the program under analysis, and uses it to generate test inputs. Saffron assumes that the grammar description is approximate since precisely describing the inputmore »
Verification is often regarded as a one-time procedure undertaken after a protocol is specified but before it is implemented. However, in practice, protocols continually evolve with the addition of new capabilities and performance optimizations. Existing verification tools are ill-suited to “tracking” protocol evolution and programmers are too busy (or too lazy?) to simultaneously co-evolve specifications manually. This means that the correctness guarantees determined at verification time can erode as protocols evolve. Existing software quality techniques such as regression testing and root cause analysis, which naturally support system evolution, are poorly suited to reasoning about fault tolerance properties of a distributed system because these properties require a search of the execution schedule rather than merely replaying inputs. This paper advocates that our community should explore the intersection of testing and verification to better ensure quality for distributed software and presents our experience evolving a data replication protocol at Elastic using a novel bug-finding technology called Lineage Driven Fault Injection (LDFI) as evidence.