Summary Model‐based test design is increasingly being applied in practice and studied in research. Model‐based testing (MBT) exploits abstract models of the software behaviour to generate abstract tests, which are then transformed into concrete tests ready to run on the code. Given that abstract tests are designed to cover models but are run on code (after transformation), the effectiveness of MBT is dependent on whether model coverage also ensures coverage of key functional code. In this article, we investigate how MBT approaches generate tests from model specifications and how the coverage of tests designed strictly based on the model translates to code coverage. We used snowballing to conduct a systematic literature review. We started with three primary studies, which we refer to as the initial seeds. At the end of our search iterations, we analysed 30 studies that helped answer our research questions. More specifically, this article characterizes how test sets generated at the model level are mapped and applied to the source code level, discusses how tests are generated from the model specifications, analyses how the test coverage of models relates to the test coverage of the code when the same test set is executed and identifies the technologies and software development tasks that are on focus in the selected studies. Finally, we identify common characteristics and limitations that impact the research and practice of MBT:(i) some studies did not fully describe how tools transform abstract tests into concrete tests,(ii) some studies overlooked the computational cost of model‐based approaches and (iii) some studies found evidence that bears out a robust correlation between decision coverage at the model level and branch coverage at the code level. We also noted that most primary studies omitted essential details about the experiments.

In testing, engineers want to run the most useful tests early (prioritization). When tests are run hundreds or thousands of times, minimizing a test set can result in significant savings (minimization). This paper proposes a new analysis technique to address both the minimal test set and the test case prioritization problems. This paper precisely defines the concept of mutant stubbornness, which is the basis for our analysis technique. We empirically compare our technique with other test case minimization and prioritization techniques in terms of the size of the minimized test sets and how quickly mutants are killed. We used seven C language subjects from the Siemens Repository, specifically the test sets and the killing matrices from a previous study. We used 30 different orders for each set and ran every technique 100 times over each set. Results show that our analysis technique performed significantly better than prior techniques for creating minimal test sets and was able to establish new bounds for all cases. Also, our analysis technique killed mutants as fast or faster than prior techniques. These results indicate that our mutant stubbornness technique constructs test sets that are both minimal in size, and prioritized effectively, as well or better than other techniques.

Real-time systems must respond to events in a timely fashion; in hard real-time systems the penalty for a missed deadline is high. It is therefore necessary to design hard real-time systems so that the timing behavior of the tasks can be predicted. Static real-time systems have prior knowledge of the worst-case arrival patterns and resource usage. Therefore, a schedule can be calculated off-line and tasks can be guaranteed to have sufficient resources to complete (resource adequacy). Dynamic real-time systems, on the other hand, do not have such prior knowledge, and therefore must react to events when they occur. They also must adapt to changes in the urgencies of various tasks, and fairly allocate resources among the tasks. A disadvantage of static real-time systems is that a requirement on resource adequacy makes them expensive and often impractical. Dynamic realtime systems, on the other hand, have the disadvantage of being less predictable and therefore difficult to test. Hence, in dynamic systems, timeliness is hard to guarantee and reliability is often low. Using a constrained execution environment, we attempt to increase the testability of such systems. An initial step is to identify factors that affect testability. We present empirical results on how various factors in the execution environment impacts testability of real-time systems. The results show that some of the factors, previously identified as possibly impacting testability, do not have an impact, while others do.

We argue that architecural ideas from DeeDS, a distributed real-time databases, can be used to create a whiteboard infrastructure usable in information fusion applications as well as in fault tolerant simulations. We discuss the need for fault-tolerant real-time simulation in information fusion, and how this can be supported by the whiteboard infrastructure. There are many reasons to perform real-time simulations (including environment simulation) - the use of real hardware or systems can be prohibitively expensive or dangerous to people and equipment, or may simply not be available at all. The combination of real and simulated objects, or nodes, is of particular interest when it is prohibitively expensive, dangerous, or impossible to test sufficient numbers of collaborating nodes in a realistic application. The need for fault tolerance of such real-time simulations, with a mixture of real and simulated nodes, occurs because it is difficult/impossible to restart physical nodes or wait for restart of simulated nodes, if one or more simulation instances fail. The key problem when mixing real and simulated nodes is the occurrence of "external actions" that cannot easily be undone and redone when a failure has occurred. We describe a natural approach to support multiple degrees of fault tolerance in realtime simulations, based on optimistic synchronization on top of a whiteboard architecture. The whiteboard architecture is a natural and useful infrastructure metaphor for information fusion, an information-based exchange that allows real and simulated nodes to be freely mixed.

We propose virtual full replication by adaptive segmentation (ViFuR-A), and evaluate its ability to maintain scalability in a replicated real-time database. With full replication and eventual consistency, transaction timeliness becomes independent of network delays for all transactions. However, full replication does not scale well, since all updates must be replicated to all nodes, also when data is needed only at a subset of the nodes. With virtual full replication that adapts to actual data needs, resource usage can be bounded and the database can be made scalable. We propose a scheme for adaptive segmentation that detects new data needs and adapts replication. The scheme includes an architecture, a scalable protocol and a replicated directory service that together maintains scalability. We show that adaptive segmentation bounds the required storage at a significantly lower level compared to static segmentation, for a typical workload where the data needs change repeatedly. Adaptation time can be kept constant for the workload when there are sufficient resources. Also, the storage is constant with an increasing amount of nodes and linear with an increasing rate of change to data needs.

This paper provides an overview of a collaborative research program in information fusion from databases, sensors and simulations. Information fusion entails the combination of data from multiple sources, to generate information that cannot be derived from the individual sources. This area is of strategic importance for industry and defense, as well as public administration areas such as health care, and needs to be pursued as an academic subject. A large number of industrial partners are supporting and participating in the development of the area. The paper describes the program’s general approach and main research areas, with a particular focus on the role of information fusion in systems development.

Combination strategies are test case selection methods that identify test cases by combining values of the different test object input parameters based on some combinatorial strategy. This survey presents 16 different combination strategies, covering more than 40 papers that focus on one or several combination strategies. This collection represents most of the existing work performed on combination strategies. This survey describes the basic algorithms used by the combination strategies. Some properties of combination strategies, including coverage criteria and theoretical bounds on the size of test suites, are also included in this description. This survey paper also includes a subsumption hierarchy that attempts to relate the various coverage criteria associated with the identified combination strategies. Copyright © 2005 John Wiley & Sons, Ltd.

Temporal correctness is crucial to the dependability of real-time systems. Few methods exist to test for temporal correctness and most existing methods are ad-hoc. A problem with testing real-time applications is the dependency on the execution time and execution order of individual tasks. Thus, the response times for the tasks may be non-deterministic with respect to inputs. Conventional test coverage criteria ignore task interleaving and tinting and, thus do not help determine which execution orders need to be exercised to test for temporal correctness. This paper presents test criteria based on mutation to test timeliness. We also show how previously proposed methods in specification based testing, can be applied to testing real-time systems

Contemporary distributed office systems rely heavily on communication between workstations, file servers, printers, and other computerized components.The COSIE Communication Subsystem has been developed for an office system internetwork consisting of local networks of varying technologies.The communication facilities provide for the transmission of self-contained messages to mailboxes, which are referenced by capabilities and may be shared.A generalized method for pairing requests with responses supports a variety of communication paradigms, while a flexible routing algorithm permits diverse network topologies.The main emphasis in the design of the COSIE Communication Subsystem was on presenting simple mechanisms that allow more general policies and protocols to be explored.

Path expressions are a tool for synchronization of concurrent processes. They are an integral part of the data abstraction mechanism in a programming language, and specify synchronization entirely in terms of the allowable sequences of operations on an object of the abstract data type. This paper describes an attempt to push the path expression synchronization construct along three dimensions - specification, verification, and implementation - into a useful theoretical and practical tool. We define Predicate Path Expressions (PPEs), which allow for a more convenient specification of many synchronization problems. The predicate is a powerful extension to path expressions that increases their expressiveness. We formally define the semantics of PPEs by a transformation to a corresponding nondeterministic program, thus allowing the use of known verification techniques for nondeterministic programs to be used for proving properties of the PPE and the data abstraction of which it is a part. We also describe our existing implementation, in Algol 68, of a data abstraction mechanism that incorporates PPEs.