Checking experiments for stream X-machines

This article is a post-print version of the published article which may be accessed at the link below. Copyright © 2010 Elsevier B.V. All rights reserved.Stream X-machines are a state based formalism that has associated with it a particular development process in which a system is built from trusted components. Testing thus essentially checks that these components have been combined in a correct manner and that the orders in which they can occur are consistent with the specification. Importantly, there are test generation methods that return a checking experiment: a test that is guaranteed to determine correctness as long as the implementation under test (IUT) is functionally equivalent to an unknown element of a given fault domain Ψ. Previous work has show how three methods for generating checking experiments from a finite state machine (FSM) can be adapted to testing from a stream X-machine. However, there are many other methods for generating checking experiments from an FSM and these have a variety of benefits that correspond to different testing scenarios. This paper shows how any method for generating a checking experiment from an FSM can be adapted to generate a checking experiment for testing an implementation against a stream X-machine. This is the case whether we are testing to check that the IUT is functionally equivalent to a specification or we are testing to check that every trace (input/output sequence) of the IUT is also a trace of a nondeterministic specification. Interestingly, this holds even if the fault domain Ψ used is not that traditionally associated with testing from a stream X-machine. The results also apply for both deterministic and nondeterministic implementations

Testing conformance of a deterministic implementation against a non-deterministic stream X-machine

Stream X-machines are a formalisation of extended finite state machines that have been used to specify systems. One of the great benefits of using stream X-machines, for the purpose of specification, is the associated test generation technique which produces a test that is guaranteed to determine correctness under certain design for test conditions. This test generation algorithm has recently been extended to the case where the specification is non-deterministic. However, the algorithms for testing from a non-deterministic stream X-machine currently have limitations: either they test for equivalence, rather than conformance or they restrict the source of non-determinism allowed in the specification. This paper introduces a new test generation algorithm that overcomes both of these limitations, for situations where the implementation is known to be deterministic

Wreath Products of Permutation Classes

A permutation class which is closed under pattern involvement may be described in terms of its basis. The wreath product construction X \wr Y of two permutation classes X and Y is also closed, and we investigate classes Y with the property that, for any finitely based class X, the wreath product X \wr Y is also finitely based.Comment: 14 page

Distributive Laws and Decidable Properties of SOS Specifications

Some formats of well-behaved operational specifications, correspond to natural transformations of certain types (for example, GSOS and coGSOS laws). These transformations have a common generalization: distributive laws of monads over comonads. We prove that this elegant theoretical generalization has limited practical benefits: it does not translate to any concrete rule format that would be complete for specifications that contain both GSOS and coGSOS rules. This is shown for the case of labeled transition systems and deterministic stream systems.Comment: In Proceedings EXPRESS/SOS 2014, arXiv:1408.127

Testing a deterministic implementation against a non-controllable non-deterministic stream X-machine

A stream X-machine is a type of extended finite state machine with an associated development approach that consists of building a system from a set of trusted components. One of the great benefits of using stream X-machines for the purpose of specification is the existence of test generation techniques that produce test suites that are guaranteed to determine correctness as long as certain well-defined conditions hold. One of the conditions that is traditionally assumed to hold is controllability: this insists that all paths through the stream X-machine are feasible. This restrictive condition has recently been weakened for testing from a deterministic stream X-machine. This paper shows how controllability can be replaced by a weaker condition when testing a deterministic system against a non-deterministic stream X-machine. This paper therefore develops a new, more general, test generation algorithm for testing from a non-deterministic stream X-machine

Interface Modeling for Quality and Resource Management

We develop an interface-modeling framework for quality and resource management that captures configurable working points of hardware and software components in terms of functionality, resource usage and provision, and quality indicators such as performance and energy consumption. We base these aspects on partially-ordered sets to capture quality levels, budget sizes, and functional compatibility. This makes the framework widely applicable and domain independent (although we aim for embedded and cyber-physical systems). The framework paves the way for dynamic (re-)configuration and multi-objective optimization of component-based systems for quality- and resource-management purposes

Amorphous slicing of extended finite state machines

Slicing is useful for many Software Engineering applications and has been widely studied for three decades, but there has been comparatively little work on slicing Extended Finite State Machines (EFSMs). This paper introduces a set of dependency based EFSM slicing algorithms and an accompanying tool. We demonstrate that our algorithms are suitable for dependence based slicing. We use our tool to conduct experiments on ten EFSMs, including benchmarks and industrial EFSMs. Ours is the first empirical study of dependence based program slicing for EFSMs. Compared to the only previously published dependence based algorithm, our average slice is smaller 40% of the time and larger only 10% of the time, with an average slice size of 35% for termination insensitive slicing