SOUFFLÉ is an open source programming framework that performs static program analysis expressed in Datalog on very large code bases, including points-to analysis on OpenJDK7 (1.4M program variables, 350K objects, 160K methods) in under a minute. SOUFFLÉ is being successfully used for Java security analyses at Oracle Labs due to (1) its high-performance, (2) support for rapid program analysis development, and (3) customizability. SOUFFLÉ incorporates the highly flexible Datalog-based program analysis paradigm while exhibiting performance results that are on-par with manually developed state-of-the-art tools. In this tool paper, we introduce the SOUFFLÉ architecture, usage and demonstrate its applicability for large-scale code analysis on the OpenJDK7 library as a use case. Parts of this research was conducted while visiting Oracle Labs, Australia as assistants and visiting professor.

Designing and crafting a static program analysis is challenging due to the complexity of the task at hand. Among the challenges are modelling the semantics of the input language, finding suitable abstractions for the analysis, and handwriting efficient code for the analysis in a traditional imperative language such as C++. Hence, the development of static program analysis tools is costly in terms of development time and resources for real world languages. To overcome, or at least alleviate the costs of developing a static program analysis, Datalog has been proposed as a domain specific language (DSL). With Datalog, a designer expresses a static program analysis in the form of a logical specification. While a domain specific language approach aids in the ease of development of program analyses, it is commonly accepted that such an approach has worse runtime performance than handcrafted static analysis tools. In this work, we introduce a new program synthesis methodology for Datalog specifications to produce highly efficient monolithic C++ analyzers. The synthesis technique requires the re-interpretation of the semi-naïve evaluation as a scaffolding for translation using partial evaluation. To achieve high-performance, we employ stagedcompilation techniques and specialize the underlying relational data structures for a given Datalog specification. Experimentation on benchmarks for large-scale program analysis validates the superior performance of our approach over available Datalog tools and demonstrates our competitiveness with state-of-the-art handcrafted tools.

PostprintThis is the accepted version of a paper published in Acta Informatica. This paper has been peerreviewed but does not include the final publisher proof-corrections or journal pagination.Citation for the original published paper (version of record):Leroux, J., Rümmer, P., Subotic, P. (2016) Guiding Craig interpolation with domain-specific abstractions. Abstract Craig Interpolation is a standard method to construct and refine abstractions in model checking. To obtain abstractions that are suitable for the verification of software programs or hardware designs, model checkers rely on theorem provers to find the right interpolants, or interpolants containing the right predicates, in a generally infinite lattice of interpolants for any given interpolation problem. We present a semantic and solver-independent framework for systematically exploring interpolant lattices, based on the notion of interpolation abstraction. We discuss how interpolation abstractions can be constructed for a variety of logics, and how they can be applied in the context of software model checking.

Cloud services provide the ability to provision virtual networked infrastructure on demand over the Internet. The rapid growth of these virtually provisioned cloud networks has increased the demand for automated reasoning tools capable of identifying misconfigurations or security vulnerabilities. This type of automation gives customers the assurance they need to deploy sensitive workloads. It can also reduce the cost and time-to-market for regulated customers looking to establish compliance certification for cloud-based applications. In this industrial case-study, we describe a new network reachability reasoning tool, called Tiros, that uses off-the-shelf automated theorem proving tools to fill this need. Tiros is the foundation of a recently introduced network security analysis feature in the Amazon Inspector service now available to millions of customers building applications in the cloud. Tiros is also used within Amazon Web Services (AWS) to automate the checking of compliance certification and adherence to security invariants for many AWS services that build on existing AWS networking features.