Using formal methods to support testing

Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing

Test generation from P systems using model checking

This paper presents some testing approaches based on model checking and using different testing criteria. First, test sets are built from different Kripke structure representations. Second, various rule coverage criteria for transitional, non-deterministic, cell-like P systems, are considered in order to generate adequate test sets. Rule based coverage criteria (simple rule coverage, context-dependent rule coverage and variants) are defined and, for each criterion, a set of LTL (Linear Temporal Logic) formulas is provided. A codification of a P system as a Kripke structure and the sets of LTL properties are used in test generation: for each criterion, test cases are obtained from the counterexamples of the associated LTL formulas, which are automatically generated from the Kripke structure codification of the P system. The method is illustrated with an implementation using a specific model checker, NuSMV. (C) 2010 Elsevier Inc. All rights reserved

Towards Statistical Prioritization for Software Product Lines Testing

Software Product Lines (SPL) are inherently difficult to test due to the combinatorial explosion of the number of products to consider. To reduce the number of products to test, sampling techniques such as combinatorial interaction testing have been proposed. They usually start from a feature model and apply a coverage criterion (e.g. pairwise feature interaction or dissimilarity) to generate tractable, fault-finding, lists of configurations to be tested. Prioritization can also be used to sort/generate such lists, optimizing coverage criteria or weights assigned to features. However, current sampling/prioritization techniques barely take product behavior into account. We explore how ideas of statistical testing, based on a usage model (a Markov chain), can be used to extract configurations of interest according to the likelihood of their executions. These executions are gathered in featured transition systems, compact representation of SPL behavior. We discuss possible scenarios and give a prioritization procedure illustrated on an example.Comment: Extended version published at VaMoS '14 (http://dx.doi.org/10.1145/2556624.2556635

Conformance Testing with Labelled Transition Systems: Implementation Relations and Test Generation

This paper studies testing based on labelled transition systems, presenting two test generation algorithms with their corresponding implementation relations. The first algorithm assumes that implementations communicate with their environment via symmetric, synchronous interactions. It is based on the theory of testing equivalence and preorder, as is most of the testing theory for labelled transition systems, and it is found in the literature in some slightly different variations. The second algorithm is based on the assumption that implementations communicate with their environment via inputs and outputs. Such implementations are formalized by restricting the class of labelled transition systems to those systems that can always accept input actions. For these implementations a testing theory is developed, analogous to the theory of testing equivalence and preorder. It consists of implementation relations formalizing the notion of conformance of these implementations with respect to labelled transition system specifications, test cases and test suites, test execution, the notion of passing a test suite, and the test generation algorithm, which is proved to produce sound test suites for one of the implementation relations

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

Testing Identifiable Kernel P Systems Using an X-machine Approach

This paper presents a testing approach for kernel P systems (kP systems), based on the X-machine testing framework and the concept of cover automaton. The testing methodology ensures that the implementation conforms the speci cations, under certain conditions, such as the identi ably concept in the context of kernel P systems

The complexity of asynchronous model based testing

This is the post-print version of the final paper published in Theoretical Computer Science. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.In model based testing (MBT), testing is based on a model MM that typically is expressed using a state-based language such as an input output transition system (IOTS). Most approaches to MBT assume that communications between the system under test (SUT) and its environment are synchronous. However, many systems interact with their environment through asynchronous channels and the presence of such channels changes the nature of testing. In this paper we investigate the situation in which the SUT interacts with its environment through asynchronous channels and the problems of producing test cases to reach a state, execute a transition, or to distinguish two states. In addition, we investigate the Oracle Problem. All four problems are explored for both FIFO and non-FIFO channels. It is known that the Oracle Problem can be solved in polynomial time for FIFO channels but we also show that the three test case generation problems can also be solved in polynomial time in the case where the IOTS is observable but the general test generation problems are EXPTIME-hard. For non-FIFO channels we prove that all of the test case generation problems are EXPTIME-hard and the Oracle Problem in NP-hard, even if we restrict attention to deterministic IOTSs

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

Mutation testing from probabilistic finite state machines

Mutation testing traditionally involves mutating a program in order to produce a set of mutants and using these mutants in order to either estimate the effectiveness of a test suite or to drive test generation. Recently, however, this approach has been applied to specifications such as those written as finite state machines. This paper extends mutation testing to finite state machine models in which transitions have associated probabilities. The paper describes several ways of mutating a probabilistic finite state machine (PFSM) and shows how test sequences that distinguish between a PFSM and its mutants can be generated. Testing then involves applying each test sequence multiple times, observing the resultant output sequences and using results from statistical sampling theory in order to compare the observed frequency of each output sequence with that expected