In this paper we introduce a novel method for improving static analysis of real code by using dynamic analysis. We have implemented our technique to enhance the Infer static analyzer [6] for Erlang by supplementing its analysis with data obtained by FAUSTA [24] dynamic analysis. We present the technical details of the algorithm combining static and dynamic analysis and a case study on its evaluation on WhatsApp's Erlang code to detect software defects. Results show an increase in detected bugs in 76% of the runs when data from dynamic analysis is used. In particular, on average, data provided by dynamic analysis for 1 function enables static analysis of 2.1 additional functions. Moreover, dynamic data enabled analysis of a property not verifiable using static analysis alone.

We introduce Fausta, an algorithmic traffic gener-ation platform that enables analysis and testing at scale. Fausta has been deployed at Meta to analyze and test the WhatsApp plat-form infrastructure since September 2020, enabling WhatsApp developers to deploy reliable code changes to a code base of millions of lines of code, supporting over 2 billion users who rely on WhatsApp for their daily communications. Fausta covers expected and unexpected program behaviors in a privacy-safe controlled environment to support multiple use cases such as reliability testing, privacy analysis and performance regression detection. It currently supports three different algorithmic input generation strategies, each of which construct realistic backend server traffic that closely simulates production data, without replaying any real user data. Fausta has been deployed and closely integrated into the WhatsApp continuous integration process, catching bugs in development before they hit production. We report on the development and deployment of Fausta's reliability use case between September 2020 and August 2021. During this period it has found 1,876 unique reliability issues, with a fix rate of 74%, indicating a high degree of true positive fault revelation. We also report on the distribution of fault types revealed by Fausta, and the correlation between coverage and faults found. Overall, we do find evidence that higher coverage is correlated with fault revelation.

Abstract Theta is a model checking framework based on abstraction refinement algorithms. In SV-COMP 2022, we introduce: 1) reasoning at the source-level via a direct translation from C programs; 2) support for concurrent programs with interleaving semantics; 3) mitigation for non-progressing refinement loops; 4) support for SMT-LIB-compliant solvers. We combine all of the aforementioned techniques into a portfolio with dynamic algorithm selection.

Abstract Gazer-Theta is a software model checking toolchain including various analyses for state reachability. The frontend, namely Gazer, supports C programs through an LLVM-based transformation and optimization pipeline. Gazer includes an integrated bounded model checker (BMC) and can also employ the Theta backend, a generic verification framework based on abstraction-refinement (CEGAR). On SV-COMP 2021, a portfolio of BMC, explicit-value analysis, and predicate abstraction is applied sequentially in this order.

Abstract Solidity is the dominant programming language for Ethereum smart contracts. This paper presents a high-level formalization of the Solidity language with a focus on the memory model. The presented formalization covers all features of the language related to managing state and memory. In addition, the formalization we provide is effective: all but few features can be encoded in the quantifier-free fragment of standard SMT theories. This enables precise and efficient reasoning about the state of smart contracts written in Solidity. The formalization is implemented in the SOLC-VERIFY verifier and we provide an extensive set of tests that covers the breadth of the required semantics. We also provide an evaluation on the test set that validates the semantics and shows the novelty of the approach compared to other Solidity-level contract analysis tools.

Abstract Automated formal verification is often based on the Counterexample-Guided Abstraction Refinement (CEGAR) approach. Many variants of CEGAR have been developed over the years as different problem domains usually require different strategies for efficient verification. This has lead to generic and configurable CEGAR frameworks, which can incorporate various algorithms. In our paper we propose six novel improvements to different aspects of the CEGAR approach, including both abstraction and refinement. We implement our new contributions in the Theta framework allowing us to compare them with state-of-the-art algorithms. We conduct an experiment on a diverse set of models to address research questions related to the effectiveness and efficiency of our new strategies. Results show that our new contributions perform well in general. Moreover, we highlight certain cases where performance could not be increased or where a remarkable improvement is achieved.

In this paper, we present Theta, a configurable model checking framework. The goal of the framework is to support the design, execution and evaluation of abstraction refinement-based reachability analysis algorithms for models of different formalisms. It enables the definition of input formalisms, abstract domains, model interpreters, and strategies for abstraction and refinement. Currently it contains front-end support for transition systems, control flow automata and timed automata. The built-in abstract domains include predicates, explicit values, zones and their combinations, along with various refinement strategies implemented for each. The configurability of the framework allows the integration of several abstraction and refinement methods, this way supporting the evaluation of their advantages and shortcomings. We demonstrate the applicability of the framework by use cases for the safety checking of PLC, hardware, C programs and timed automata models.

PetriDotNet is an extensible Petri net editor and analysis tool originally developed to support the education of formal methods. The ease of use and simple extensibility fostered more and more algorithmic developments. Thanks to the continuous interest of developers (especially M.Sc. and Ph.D. students who choose PetriDotNet as the framework of their thesis project), by now PetriDotNet became an analysis platform, providing various cutting-edge model checking algorithms and stochastic analysis algorithms. As a result, industrial application of the tool also emerged in recent years. In this paper we overview the main features and the architecture of PetriDotNet , and compare it with other available tools.