Clients rely on database systems to be correct, which requires the system not only to implement transactions’ semantics correctly but also to provide isolation guarantees for the transactions. This paper presents a clientcentric technique for checking both semantic correctness and isolation-level guarantees for black-box database systems based on observations collected from running transactions on these systems. Our technique verifies observational correctness with respect to a given set of transactions and observations for them, which holds iff there exists a possible correct execution of the transactions under a given isolation level that could result in these observations. Our technique relies on novel symbolic encodings of (1) the semantic correctness of database transactions in the presence of weak isolation and (2) isolation-level guarantees. These are used by the checker to query a Satisfiability Modulo Theories solver. We applied our tool Troubadour to verify observational correctness of several database systems, including PostgreSQL and an industrial system under development, in which the tool helped detect two new bugs. We also demonstrate that Troubadour is able to find known semantic correctness bugs and detect isolation-related anomalies.

Verification of distributed systems using systematic exploration is daunting because of the many possible interleavings of messages and failures. When faced with this scalability challenge, existing approaches have traditionally mitigated state space explosion by avoiding exploration of redundant states (e.g., via state hashing) and redundant interleavings of transitions (e.g., via partial-order reductions). In this paper, we present an efficient symbolic exploration method that not only avoids redundancies in states and interleavings, but additionally avoids redundant computations that are performed during updates to states on transitions. Our symbolic explorer leverages a novel, fine-grained, canonical representation of distributed system configurations (states) to identify opportunities for avoiding such redundancies on-the-fly. The explorer also includes an interface that is compatible with abstractions for state-space reduction and with partial-order and other reductions for avoiding redundant interleavings. We implement our approach in the tool Psym and empirically demonstrate that it outperforms a state-of-the-art exploration tool, can successfully verify many common distributed protocols, and can scale to multiple real-world industrial case studies across

Near-term quantum computers are expected to work in an environment where each operation is noisy, with no error correction. Therefore, quantum-circuit optimizers are applied to minimize the number of noisy operations. Today, physicists are constantly experimenting with novel devices and architectures. For every new physical substrate and for every modification of a quantum computer, we need to modify or rewrite major pieces of the optimizer to run successful experiments. In this paper, we present QUESO, an efficient approach for automatically synthesizing a quantum-circuit optimizer for a given quantum device. For instance, in 1.2 minutes, QUESO can synthesize an optimizer with high-probability correctness guarantees for IBM computers that significantly outperforms leading compilers, such as IBM's Qiskit and TKET, on the majority (85%) of the circuits in a diverse benchmark suite. A number of theoretical and algorithmic insights underlie QUESO: (1) An algebraic approach for representing rewrite rules and their semantics. This facilitates reasoning about complex symbolic rewrite rules that are beyond the scope of existing techniques. (2) A fast approach for probabilistically verifying equivalence of quantum circuits by reducing the problem to a special form of polynomial identity testing . (3) A novel probabilistic data structure, called a polynomial identity filter (PIF), for efficiently synthesizing rewrite rules. (4) A beam-search-based algorithm that efficiently applies the synthesized symbolic rewrite rules to optimize quantum circuits.

Near-term quantum computers will operate in a noisy environment, without error correction. A critical problem for near-term quantum computing is laying out a logical circuit onto a physical device with limited connectivity between qubits. This is known as the qubit mapping and routing (QMR) problem, an intractable combinatorial problem. It is important to solve QMR as optimally as possible to reduce the amount of added noise, which may render a quantum computation useless. In this paper, we present a novel approach for optimally solving the QMR problem via a reduction to maximum satisfiability (MAXSAT). Additionally, we present two novel relaxation ideas that shrink the size of the MAXSAT constraints by exploiting the structure of a quantum circuit. Our thorough empirical evaluation demonstrates (1) the scalability of our approach compared to state-of-the-art optimal QMR techniques (solves more than 3x benchmarks with 40x speedup), (2) the significant cost reduction compared to state-of-the-art heuristic approaches (an average of ~5x swap reduction), and (3) the power of our proposed constraint relaxations.

Weak supervision (WS) is a powerful method to build labeled datasets for training supervised models in the face of little-to-no labeled data. It replaces hand-labeling data with aggregating multiple noisy-but-cheap label estimates expressed by labeling functions (LFs). While it has been used successfully in many domains, weak supervision's application scope is limited by the difficulty of constructing labeling functions for domains with complex or high-dimensional features. To address this, a handful of methods have proposed automating the LF design process using a small set of ground truth labels. In this work, we introduce AutoWS-Bench-101: a framework for evaluating automated WS (AutoWS) techniques in challenging WS settings---a set of diverse application domains on which it has been previously difficult or impossible to apply traditional WS techniques. While AutoWS is a promising direction toward expanding the application-scope of WS, the emergence of powerful methods such as zero-shot foundation models reveal the need to understand how AutoWS techniques compare or cooperate with modern zero-shot or few-shot learners. This informs the central question of AutoWS-Bench-101: given an initial set of 100 labels for each task, we ask whether a practitioner should use an AutoWS method to generate additional labels or use some simpler baseline, such as zero-shot predictions from a foundation model or supervised learning. We observe that it is necessary for AutoWS methods to incorporate signal from foundation models if they are to outperform simple few-shot baselines, and AutoWS-Bench-101 promotes future research in this direction. We conclude with a thorough ablation study of AutoWS methods.

Verifying secure information flow by reducing it to safety verification is a popular approach, based on constructing product programs or self-compositions of given programs.However, most such existing efforts are non-modular, i.e., they do not infer relational specifications for procedures in interprocedural programs.Such relational specifications can help to verify security properties in a modular fashion, e.g., for verifying clients of library APIs.They also provide security contracts at procedure boundaries to aid code understanding and maintenance.There has been recent interest in constructing modular product programs, but where users are required to provide procedure summaries and related annotations.In this work, we propose to automatically infer relational specifications for procedures in modular product programs.Our approach uses syntax-guided synthesis techniques and grammar templates that target verification of secure information flow properties.This enables automation of modular verification for such properties, thereby reducing the annotation burden.We have implemented our techniques on top of a solver for constrained Horn clauses (CHC).Our evaluation demonstrates that our tool is capable of inferring adequate relational specifications for procedures without requiring annotations.Furthermore, it outperforms an existing state-of-the-art hyperproperty verifier and a modular CHC-based verifier on benchmarks with loops or recursion.

Relational safety specifications describe multiple runs of the same program or relate the behaviors of multiple programs. Approaches to automatic relational verification often compose the programs and analyze the result for safety, but a naively composed program can lead to difficult verification problems. We propose to exploit relational specifications for simplifying the generated verification subtasks. First, we maximize opportunities for synchronizing code fragments. Second, we compute symmetries in the specifications to reveal and avoid redundant subtasks. We have implemented these enhancements in a prototype for verifying k -safety properties on Java programs. Our evaluation confirms that our approach leads to a consistent performance speedup on a range of benchmarks. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.