Performance Evaluation of Software using Formal Methods

Formal Methods (FMs) can be used in varied areas of applications and to solve critical and fundamental problems of Performance Evaluation (PE). Modelling and analysis techniques can be used for both system and software performance evaluation. The functional features and performance properties of modern software used for performance evaluation has become so intertwined. Traditional models and methods for performance evaluation has been studied widely which culminated into the modern models and methods for system and software engineering evaluation such as formal methods. Techniques have transcended from functionality to performance modeling and analysis. Formal models help in identifying faulty reasoning far earlier than in traditional design; and formal specification has proved useful even on already existing software and systems. Formal approach eliminates ambiguity. The basic and final goal of the performance evaluation technique is to come to a conclusion, whether the software and system are working in a good condition or satisfactorily

QuantUM: Quantitative Safety Analysis of UML Models

When developing a safety-critical system it is essential to obtain an assessment of different design alternatives. In particular, an early safety assessment of the architectural design of a system is desirable. In spite of the plethora of available formal quantitative analysis methods it is still difficult for software and system architects to integrate these techniques into their every day work. This is mainly due to the lack of methods that can be directly applied to architecture level models, for instance given as UML diagrams. Also, it is necessary that the description methods used do not require a profound knowledge of formal methods. Our approach bridges this gap and improves the integration of quantitative safety analysis methods into the development process. All inputs of the analysis are specified at the level of a UML model. This model is then automatically translated into the analysis model, and the results of the analysis are consequently represented on the level of the UML model. Thus the analysis model and the formal methods used during the analysis are hidden from the user. We illustrate the usefulness of our approach using an industrial strength case study.Comment: In Proceedings QAPL 2011, arXiv:1107.074

A Unified View of Piecewise Linear Neural Network Verification

The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models. Despite the reputation of learned NN models to behave as black boxes and the theoretical hardness of proving their properties, researchers have been successful in verifying some classes of models by exploiting their piecewise linear structure and taking insights from formal methods such as Satisifiability Modulo Theory. These methods are however still far from scaling to realistic neural networks. To facilitate progress on this crucial area, we make two key contributions. First, we present a unified framework that encompasses previous methods. This analysis results in the identification of new methods that combine the strengths of multiple existing approaches, accomplishing a speedup of two orders of magnitude compared to the previous state of the art. Second, we propose a new data set of benchmarks which includes a collection of previously released testcases. We use the benchmark to provide the first experimental comparison of existing algorithms and identify the factors impacting the hardness of verification problems.Comment: Updated version of "Piecewise Linear Neural Network verification: A comparative study

Distributed Real-Time Emulation of Formally-Defined Patterns for Safe Medical Device Control

Safety of medical devices and of their interoperation is an unresolved issue causing severe and sometimes deadly accidents for patients with shocking frequency. Formal methods, particularly in support of highly reusable and provably safe patterns which can be instantiated to many device instances can help in this regard. However, this still leaves open the issue of how to pass from their formal specifications in logical time to executable emulations that can interoperate in physical time with other devices and with simulations of patient and/or doctor behaviors. This work presents a specification-based methodology in which virtual emulation environments can be easily developed from formal specifications in Real-Time Maude, and can support interactions with other real devices and with simulation models. This general methodology is explained in detail and is illustrated with two concrete scenarios which are both instances of a common safe formal pattern: one scenario involves the interaction of a provably safe pacemaker with a simulated heart; the other involves the interaction of a safe controller for patient-induced analgesia with a real syringe pump.Comment: In Proceedings RTRTS 2010, arXiv:1009.398

Formal Methods for Autonomous Systems

Formal methods refer to rigorous, mathematical approaches to system development and have played a key role in establishing the correctness of safety-critical systems. The main building blocks of formal methods are models and specifications, which are analogous to behaviors and requirements in system design and give us the means to verify and synthesize system behaviors with formal guarantees. This monograph provides a survey of the current state of the art on applications of formal methods in the autonomous systems domain. We consider correct-by-construction synthesis under various formulations, including closed systems, reactive, and probabilistic settings. Beyond synthesizing systems in known environments, we address the concept of uncertainty and bound the behavior of systems that employ learning using formal methods. Further, we examine the synthesis of systems with monitoring, a mitigation technique for ensuring that once a system deviates from expected behavior, it knows a way of returning to normalcy. We also show how to overcome some limitations of formal methods themselves with learning. We conclude with future directions for formal methods in reinforcement learning, uncertainty, privacy, explainability of formal methods, and regulation and certification