Well Structured Transition Systems with History

We propose a formal model of concurrent systems in which the history of a computation is explicitly represented as a collection of events that provide a view of a sequence of configurations. In our model events generated by transitions become part of the system configurations leading to operational semantics with historical data. This model allows us to formalize what is usually done in symbolic verification algorithms. Indeed, search algorithms often use meta-information, e.g., names of fired transitions, selected processes, etc., to reconstruct (error) traces from symbolic state exploration. The other interesting point of the proposed model is related to a possible new application of the theory of well-structured transition systems (wsts). In our setting wsts theory can be applied to formally extend the class of properties that can be verified using coverability to take into consideration (ordered and unordered) historical data. This can be done by using different types of representation of collections of events and by combining them with wsts by using closure properties of well-quasi orderings.Comment: In Proceedings GandALF 2015, arXiv:1509.0685

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

Deciding branching time properties for asynchronous programs

AbstractAsynchronous programming is a paradigm that supports asynchronous function calls in addition to synchronous function calls. Programs in such a setting can be modeled by automata with counters that keep track of the number of pending asynchronous calls for each function, as well as a call stack for synchronous recursive computation. These programs have the restriction that an asynchronous call is processed only when the call stack is empty. The decidability of the control state reachability problem for such systems was recently established. In this paper, we consider the problems of checking other branching time properties for such systems. Specifically we consider the following problems — termination, which asks if there is an infinite (non-terminating) computation exhibited by the system; control state maintainability, which asks if there is a maximal execution of the system, where all the state visited lie in some “good” set; whether the system can be simulated by a given finite state system; and whether the system can simulate a given finite state system. We present decision algorithms for all these problems