RacerD: compositional static race detection

Automatic static detection of data races is one of the most basic problems in reasoning about concurrency. We present RacerD—a static program analysis for detecting data races in Java programs which is fast, can scale to large code, and has proven effective in an industrial software engineering scenario. To our knowledge, RacerD is the first inter-procedural, compositional data race detector which has been empirically shown to have non-trivial precision and impact. Due to its compositionality, it can analyze code changes quickly, and this allows it to perform continuous reasoning about a large, rapidly changing codebase as part of deployment within a continuous integration ecosystem. In contrast to previous static race detectors, its design favors reporting high-confidence bugs over ensuring their absence. RacerD has been in deployment for over a year at Facebook, where it has flagged over 2500 issues that have been fixed by developers before reaching production. It has been important in enabling the development of new code as well as fixing old code: it helped support the conversion of part of the main Facebook Android app from a single-threaded to a multi-threaded architecture. In this paper we describe RacerD’s design, implementation, deployment and impact

RacerD: compositional static race detection

Automatic static detection of data races is one of the most basic problems in reasoning about concurrency. We present RacerD—a static program analysis for detecting data races in Java programs which is fast, can scale to large code, and has proven effective in an industrial software engineering scenario. To our knowledge, RacerD is the first inter-procedural, compositional data race detector which has been empirically shown to have non-trivial precision and impact. Due to its compositionality, it can analyze code changes quickly, and this allows it to perform continuous reasoning about a large, rapidly changing codebase as part of deployment within a continuous integration ecosystem. In contrast to previous static race detectors, its design favors reporting high-confidence bugs over ensuring their absence. RacerD has been in deployment for over a year at Facebook, where it has flagged over 2500 issues that have been fixed by developers before reaching production. It has been important in enabling the development of new code as well as fixing old code: it helped support the conversion of part of the main Facebook Android app from a single-threaded to a multi-threaded architecture. In this paper we describe RacerD’s design, implementation, deployment and impact

Programming Languages and Systems

This open access book constitutes the proceedings of the 29th European Symposium on Programming, ESOP 2020, which was planned to take place in Dublin, Ireland, in April 2020, as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The actual ETAPS 2020 meeting was postponed due to the Corona pandemic. The papers deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

Formal Verification of Industrial Software and Neural Networks

Software ist ein wichtiger Bestandteil unsere heutige Gesellschaft. Da Software vermehrt in sicherheitskritischen Bereichen angewandt wird, müssen wir uns auf eine korrekte und sichere Ausführung verlassen können. Besonders eingebettete Software, zum Beispiel in medizinischen Geräten, Autos oder Flugzeugen, muss gründlich und formal geprüft werden. Die Software solcher eingebetteten Systeme kann man in zwei Komponenten aufgeteilt. In klassische (deterministische) Steuerungssoftware und maschinelle Lernverfahren zum Beispiel für die Bilderkennung oder Kollisionsvermeidung angewandt werden. Das Ziel dieser Dissertation ist es den Stand der Technik bei der Verifikation von zwei Hauptkomponenten moderner eingebetteter Systeme zu verbessern: in C/C++ geschriebene Software und neuronalen Netze. Für beide Komponenten wird das Verifikationsproblem formal definiert und neue Verifikationsansätze werden vorgestellt

Programming Languages and Systems

This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

Computer Aided Verification

The open access two-volume set LNCS 12224 and 12225 constitutes the refereed proceedings of the 32st International Conference on Computer Aided Verification, CAV 2020, held in Los Angeles, CA, USA, in July 2020.* The 43 full papers presented together with 18 tool papers and 4 case studies, were carefully reviewed and selected from 240 submissions. The papers were organized in the following topical sections: Part I: AI verification; blockchain and Security; Concurrency; hardware verification and decision procedures; and hybrid and dynamic systems. Part II: model checking; software verification; stochastic systems; and synthesis. *The conference was held virtually due to the COVID-19 pandemic

Algorithmic Resource Verification

Static estimation of resource utilisation of programs is a challenging and important problem with numerous applications. In this thesis, I present new algorithms that enable users to specify and verify their desired bounds on resource usage of functional programs. The resources considered are algorithmic resources such as the number of steps needed to evaluate a program (steps) and the number of objects allocated in the memory (alloc). These resources are agnostic to the runtimes on which the programs are executed yet provide a concrete estimate of the resource usage of an implementation. Our system is designed to handle sophisticated functional programs that use recursive functions, datatypes, closures, memoization and lazy evaluation. In our approach, users can specify in the contracts of functions an upper bound they expect to hold on the resource usages of the functions. The upper bounds can be expressed as templates with numerical holes. For example, a bound steps †?*size(inp)+? denotes that the number of evaluation steps is linear in the size of the input. The templates can be seamlessly combined with correctness invariants or preconditions necessary for establishing the bounds. Furthermore, the resource templates and invariants are allowed to use recursive and first-class functions as well as other features of the language. Our approach for verifying such resource templates operates in two phases. It first reduces the problem of resource inference to invariant inference by synthesizing an instrumented first-order program that accurately models the resource usage of the program components, the higher-order control flow and the effects of memoization, using algebraic datatypes, sets and mutual recursion. The approach solves the synthesized first-order program by producing verification conditions of the form exists-forall using a modular assume/guarantee reasoning. The verification conditions are solved using a novel counterexample-driven algorithm capable of discovering strongest resource bounds belonging to the given template. I present the results of using our system to verify upper bounds on the usage of algorithmic resources that correspond to sequential and parallel execution times, as well as heap and stack memory usage. The system was evaluated on several benchmarks that include advanced functional data structures and algorithms such as balanced trees, meldable heaps, Okasakiâs lazy data structures, dynamic programming algorithms, and also compiler phases like optimizers and parsers. The evaluations show that the system is able to infer hard, nonlinear resource bounds that are beyond the capability of the existing approaches. Furthermore, the evaluations presented in this dissertation show that, when averaged over many benchmarks, the resource consumption measured at runtime is 80% of the value inferred by the system statically when estimating the number of evaluation steps and is 88% when estimating the number of heap allocations