Property-Based Testing - The ProTest Project

The ProTest project is an FP7 STREP on property based testing. The purpose of the project is to develop software engineering approaches to improve reliability of service-oriented networks; support fault-finding and diagnosis based on specified properties of the system. And to do so we will build automated tools that will generate and run tests, monitor execution at run-time, and log events for analysis. The Erlang / Open Telecom Platform has been chosen as our initial implementation vehicle due to its robustness and reliability within the telecoms sector. It is noted for its success in the ATM telecoms switches by Ericsson, one of the project partners, as well as for multiple other uses such as in facebook, yahoo etc. In this paper we provide an overview of the project goals, as well as detailing initial progress in developing property based testing techniques and tools for the concurrent functional programming language Erlang

Trajectory optimization and motion planning for quadrotors in unstructured environments

Trajectory optimization and motion planning for quadrotors in unstructured environments Coming out from university labs robots perform tasks usually navigating through unstructured environment. The realization of autonomous motion in such type of environments poses a number of challenges compared to highly controlled laboratory spaces. In unstructured environments robots cannot rely on complete knowledge of their sorroundings and they have to continously acquire information for decision making. The challenges presented are a consequence of the high-dimensionality of the state-space and of the uncertainty introduced by modeling and perception. This is even more true for aerial-robots that has a complex nonlinear dynamics a can move freely in 3D-space. To avoid this complexity a robot have to select a small set of relevant features, reason on a reduced state space and plan trajectories on short-time horizon. This thesis is a contribution towards the autonomous navigation of aerial robots (quadrotors) in real-world unstructured scenarios. The first three chapters present a contribution towards an implementation of Receding Time Horizon Optimal Control. The optimization problem for a model based trajectory generation in environments with obstacles is set, using an approach based on variational calculus and modeling the robots in the SE(3) Lie Group of 3D space transformations. The fourth chapter explores the problem of using minimal information and sensing to generate motion towards a goal in an indoor bulding-like scenario. The fifth chapter investigate the problem of extracting visual features from the environment to control the motion in an indoor corridor-like scenario. The last chapter deals with the problem of spatial reasoning and motion planning using atomic proposition in a multi-robot environments with obstacles

IST Austria Technical Report

Gist is a tool that (a) solves the qualitative analysis problem of turn-based probabilistic games with ω-regular objectives; and (b) synthesizes reasonable environment assumptions for synthesis of unrealizable specifications. Our tool provides efficient implementations of several reduction based techniques to solve turn-based probabilistic games, and uses the analysis of turn-based probabilistic games for synthesizing environment assumptions for unrealizable specifications

Formal synthesis of control and communication schemes

Thesis (Ph.D.)--Boston UniversityIn traditional motion planning, the problem is simply specified as "go from A to B while avoiding obstacles", where A and B are two configurations or regions of interest in the robot workspace. However, a large number of robotic applications require more expressive specification languages, which allow for logical and temporal statements about the satisfaction of properties of interest. Examples include "visit A and B infinitely often, always avoid C, and do not visit D unless E vas visited before". Such task specifications cannot be trivially converted to a sequence of "go from A to B" primitives. This thesis establishes theoretical and computational frameworks for automatic synthesis of robot control and communication schemes that are correct-by-construction from task specifications given in expressive languages. We consider a purely discrete scenario, in which the dynamics of each robot is modeled as a finite discrete system. The first problem addressed in this thesis is the generation of provably-correct individual control and communication strategies for a team of robots from rich task specifications in the case when the workspace is static. The second problem relaxes this assumption and considers a scenario in which the environment changes according to some unknown patterns. It proposed a combined learning and formal synthesis approach to generate correct control policies. To tackle the first problem, we draw inspirations from the research fields of formal verification and synthesis, distributed formal synthesis, and concurrency theory. We consider a team of robots that can move among the regions of a partitioned environment and have known capabilities of servicing a set of requests that can occur in the regions of the partition. Some of these requests can be serviced by a robot individually, while some require the cooperation of groups of robots. We propose a top-down approach, in which global specifications given as Regular Expressions (RE) or Linear Temporal Logics (LTL) can be decomposed into local (individual) specifications, which can then be used to automatically synthesize robot control and communication strategies. To address the second problem, we bring together automata learning methods from the field of theoretical linguistics and techniques from temporal logic games and probabilistic model checking, to develop a provably-correct control strategy for robots moving in an environment with unknown dynamics. The robots are required to achieve a surveillance mission, in which a certain request needs to be serviced repeatedly, while the expected time in between consecutive services is minimized and additional temporal logic constraints are satisfied. We define a fragment of Linear Temporal Logic (LTL) to describe such a mission. We consider a single agent case at first and then extend the results to multi-agent systems. To this end, we apply approximate dynamic programming to our computational framework, which leads to significant reduction of computational time. To demonstrate the proposed theoretical and computational frameworks, we implement the derived algorithms in two experimental platforms, the Robotic Urban-Like Environment (RULE) and the Robotic InDoor-like Environment (RIDE). We assign tasks to the team using Regular Expressions or Linear Temporal Logics over requests occurring at regions in the environment. The robots are automatically deployed to complete the missions

Verifying Mutable Systems

Model checking has had much success in the verification of single-process and multi-process programs. However, model checkers assume an immutable topology which limits the verification in several areas. Consider the security domain, model checkers have had success in the verification of unicast structurally static protocols, but struggle to verify dynamic multicast cryptographic protocols. We give a formulation of dynamic model checking which extends traditional model checking by allowing structural changes, mutations, to the topology of multi-process network models. We introduce new mutation models when the structural mutations take either a primitive, non-primitive, or a non-deterministic form, and analyze the general complexities of each. This extends traditional model checking and allows analysis in new areas. We provide a set of proof rules to verify safety properties on dynamic models and outline its automizability. We relate dynamic models to compositional reasoning, dynamic cutoffs, parametrized analysis, and previously established parametric assertions.We provide a proof of concept by analyzing a dynamic mutual exclusion protocol and a multicast cryptography protocol

IST Austria Technical Report

We consider partially observable Markov decision processes (POMDPs) with ω-regular conditions specified as parity objectives. The class of ω-regular languages extends regular languages to infinite strings and provides a robust specification language to express all properties used in verification, and parity objectives are canonical forms to express ω-regular conditions. The qualitative analysis problem given a POMDP and a parity objective asks whether there is a strategy to ensure that the objective is satis- fied with probability 1 (resp. positive probability). While the qualitative analysis problems are known to be undecidable even for very special cases of parity objectives, we establish decidability (with optimal complexity) of the qualitative analysis problems for POMDPs with all parity objectives under finite- memory strategies. We establish asymptotically optimal (exponential) memory bounds and EXPTIME- completeness of the qualitative analysis problems under finite-memory strategies for POMDPs with parity objectives

Towards Efficient Controller Synthesis Techniques for Logical LTL Games

Two-player games are a fruitful way to represent and reason about several important synthesis tasks. These tasks include controller synthesis (where one asks for a controller for a given plant such that the controlled plant satisfies a given temporal specification), program repair (setting values of variables to avoid exceptions), and synchronization synthesis (adding lock/unlock statements in multi-threaded programs to satisfy safety assertions). In all these applications, a solution directly corresponds to a winning strategy for one of the players in the induced game. In turn, \emph{logically-specified} games offer a powerful way to model these tasks for large or infinite-state systems. Much of the techniques proposed for solving such games typically rely on abstraction-refinement or template-based solutions. In this paper, we show how to apply classical fixpoint algorithms, that have hitherto been used in explicit, finite-state, settings, to a symbolic logical setting. We implement our techniques in a tool called GenSys-LTL and show that they are not only effective in synthesizing valid controllers for a variety of challenging benchmarks from the literature, but often compute maximal winning regions and maximally-permissive controllers. We achieve \textbf{46.38X speed-up} over the state of the art and also scale well for non-trivial LTL specifications

Degeneralization Algorithm for Generation of Büchi Automata Based on Contented Situation

We present on-the-fly degeneralization algorithm used to transform generalized Büchi automata (GBA) into Büchi Automata (BA) different from the standard degeneralization algorithm. Contented situation, which is used to record what acceptance conditions are satisfiable during expanding LTL formulae, is attached to the states and transitions in the BA. In order to get the deterministic BA, the Shannon expansion is used recursively when we expand LTL formulae by applying the tableau rules. On-the-fly degeneralization algorithm is carried out in each step of the expansion of LTL formulae. Ordered binary decision diagrams are used to represent the BA and simplify LTL formulae. The temporary automata are stored as syntax directed acyclic graph in order to save storage space. These ideas are implemented in a conversion algorithm used to build a property automaton corresponding to the given LTL formulae. We compare our method to previous work and show that it is more efficient for four sets of random formulae generated by LBTT