The rise of programmable open distributed consensus platforms based on the blockchain technology has aroused a lot of interest in replicated stateful computations, aka smart contracts. As blockchains are used predominantly in financial applications, smart contracts frequently manage millions of dollars worth of virtual coins. Since smart contracts cannot be updated once deployed, the ability to reason about their correctness becomes a critical task. Yet, the de facto implementation standard, pioneered by the Ethereum platform, dictates smart contracts to be deployed in a low-level language, which renders independent audit and formal verification of deployed code infeasible in practice. We report an ongoing experiment held with an industrial blockchain vendor on designing, evaluating, and deploying Scilla, a new programming language for safe smart contracts. Scilla is positioned as an intermediate-level language, suitable to serve as a compilation target and also as an independent programming framework. Taking System F as a foundational calculus, Scilla offers strong safety guarantees by means of type soundness. It provides a clean separation between pure computational, state-manipulating, and communication aspects of smart contracts, avoiding many known pitfalls due to execution in a byzantine environment. We describe the motivation, design principles, and semantics of Scilla, and we report on Scilla use cases provided by the developer community. Finally, we present a framework for lightweight verification of Scilla programs, and showcase it with two domain-specific analyses on a suite of real-world use cases.

Pointer alias analysis is a well researched problem in the area of compilers and program verification. Many recent works in this area have focused on flow-sensitivity due to the additional precision it offers. However, a flow-sensitive analysis is computationally expensive, thus, preventing its use in larger programs. In this work, we observe that a number of object sets, consisting of tens to hundreds of objects appear together and frequently in many points-to sets. By approximating each of these object sets by a single object, we can speedup computation of points-to sets. Although the proposed approach incurs a slight loss in precision, it is shown to be safe. We use a well known data mining technique called frequent itemset mining to find these frequently occurring objects. We compare our approximation to a fully flow-sensitive pointer analysis on a set of ten benchmarks. We measure precision loss using two common client analysis queries and report an average precision loss of 0.25% on one measure and 1.40% on the other. The proposed approach results in a speedup of upto 12.9× (and an average speedup of 6.2×) in computing the points-to sets.

Precise pointer analysis is a problem of interest to both the compiler and the program verification community. Flow-sensitivity is an important dimension of pointer analysis that affects the precision of the final result computed. Scaling flow-sensitive pointer analysis to millions of lines of code is a major challenge. Recently, staged flow-sensitive pointer analysis has been proposed, which exploits a sparse representation of program code created by staged analysis. In this paper we formulate the staged flow-sensitive pointer analysis as a graph-rewriting problem. Graph-rewriting has already been used for flow-insensitive analysis. However, formulating flow-sensitive pointer analysis as a graph-rewriting problem adds additional challenges due to the nature of flow-sensitivity. We implement our parallel algorithm using Intel Threading Building Blocks and demonstrate considerable scaling (upto 2.6x) for 8 threads on a set of 10 benchmarks. Compared to the sequential implementation of staged flow-sensitive analysis, a single threaded execution of our implementation performs better in 8 of the benchmarks.