Algorithmic Verification of Asynchronous Programs

Asynchronous programming is a ubiquitous systems programming idiom to manage concurrent interactions with the environment. In this style, instead of waiting for time-consuming operations to complete, the programmer makes a non-blocking call to the operation and posts a callback task to a task buffer that is executed later when the time-consuming operation completes. A co-operative scheduler mediates the interaction by picking and executing callback tasks from the task buffer to completion (and these callbacks can post further callbacks to be executed later). Writing correct asynchronous programs is hard because the use of callbacks, while efficient, obscures program control flow. We provide a formal model underlying asynchronous programs and study verification problems for this model. We show that the safety verification problem for finite-data asynchronous programs is expspace-complete. We show that liveness verification for finite-data asynchronous programs is decidable and polynomial-time equivalent to Petri Net reachability. Decidability is not obvious, since even if the data is finite-state, asynchronous programs constitute infinite-state transition systems: both the program stack and the task buffer of pending asynchronous calls can be potentially unbounded. Our main technical construction is a polynomial-time semantics-preserving reduction from asynchronous programs to Petri Nets and conversely. The reduction allows the use of algorithmic techniques on Petri Nets to the verification of asynchronous programs. We also study several extensions to the basic models of asynchronous programs that are inspired by additional capabilities provided by implementations of asynchronous libraries, and classify the decidability and undecidability of verification questions on these extensions.Comment: 46 pages, 9 figure

Algorithms for Game Metrics

Simulation and bisimulation metrics for stochastic systems provide a quantitative generalization of the classical simulation and bisimulation relations. These metrics capture the similarity of states with respect to quantitative specifications written in the quantitative {\mu}-calculus and related probabilistic logics. We first show that the metrics provide a bound for the difference in long-run average and discounted average behavior across states, indicating that the metrics can be used both in system verification, and in performance evaluation. For turn-based games and MDPs, we provide a polynomial-time algorithm for the computation of the one-step metric distance between states. The algorithm is based on linear programming; it improves on the previous known exponential-time algorithm based on a reduction to the theory of reals. We then present PSPACE algorithms for both the decision problem and the problem of approximating the metric distance between two states, matching the best known algorithms for Markov chains. For the bisimulation kernel of the metric our algorithm works in time O(n^4) for both turn-based games and MDPs; improving the previously best known O(n^9\cdot log(n)) time algorithm for MDPs. For a concurrent game G, we show that computing the exact distance between states is at least as hard as computing the value of concurrent reachability games and the square-root-sum problem in computational geometry. We show that checking whether the metric distance is bounded by a rational r, can be done via a reduction to the theory of real closed fields, involving a formula with three quantifier alternations, yielding O(|G|^O(|G|^5)) time complexity, improving the previously known reduction, which yielded O(|G|^O(|G|^7)) time complexity. These algorithms can be iterated to approximate the metrics using binary search.Comment: 27 pages. Full version of the paper accepted at FSTTCS 200

Verification of Cyber-Physical Systems (Dagstuhl Seminar 14122)

Cyber-physical systems refer to a new genre of engineered systems consisting of a tight coupling between computation, communication and physical entities. The main focus of the seminar was to discuss issues related to the reliable development of cyber-physical systems by using formal verification. This is a multi-disciplinary area requiring collaboration between areas focusing discrete systems analysis and continuous systems analysis. To this end, the seminar brought together researchers working in the fields of formal methods, control theory and hybrid systems to identify and discuss potential issues and research questions which require collaboration between the communities. This report documents the program and the outcomes of Dagstuhl Seminar 14122 "Verification of Cyber-Physical Systems"

Games and Decisions for Rigorous Systems Engineering (Dagstuhl Seminar 12461)

This report documents the program and the outcomes of the Dagstuhl Seminar 12461 "Games and Decisions for Rigorous Systems Engineering". The seminar brought together researchers working in rigorous software engineering, with a special focus on the interaction between synthesis and automated deduction. This event was the first seminar of this kind and a kickoff of a series of seminars organised on rigorous systems engineering. The theme of the seminar was close in spirit to many events that have been held over the last decades. The talks scheduled during the seminar naturally reflected fundamental research themes of the involved communities

Time-Safety Checking for Embedded Programs

Giotto is a platform-independent language for specifying software for high-performance control applications. In this paper we present a new approach to the compilation of Giotto. Following this approach, the Giotto compiler generates code for a virtual machine, called the E machine, which can be ported to different platforms. The Giotto compiler also checks if the generated E code is time safe for a given platform, that is, if the platform offers sufficient performance to ensure that the E code is executed in a timely fashion that conforms with the Giotto semantics. Time-safet

Maize yield in smallholder agriculture system-An approach integrating socio-economic and crop management factors.

Yield gaps of maize (Zea mays L.) in the smallholder farms of eastern India are outcomes of a complex interplay of climatic variations, soil fertility gradients, socio-economic factors, and differential management intensities. Several machine learning approaches were used in this study to investigate the relative influences of multiple biophysical, socio-economic, and crop management features in determining maize yield variability using several machine learning approaches. Soil fertility status was assessed in 180 farms and paired with the surveyed data on maize yield, socio-economic conditions, and agronomic management. The C&RT relative variable importance plot identified farm size, total labor, soil factors, seed rate, fertilizer, and organic manure as influential factors. Among the three approaches compared for classifying maize yield, the artificial neural network (ANN) yielded the least (25%) misclassification on validation samples. The random forest partial dependence plots revealed a positive association between farm size and maize productivity. Nonlinear support vector machine boundary analysis for the eight top important variables revealed complex interactions underpinning maize yield response. Notably, farm size and total labor synergistically increased maize yield. Future research integrating these algorithms with empirical crop growth models and crop simulation models for ex-ante yield estimations could result in further improvement