Summary Ships and icebergs are similar in size and intensity in SAR images, so it is difficult to distinguish them in remote sensing images. Deep learning is a technique based on neural networks, which has played an important role in image information processing. In order to address the challenge of ship and iceberg classification, we present a convolutional neural network (CNN) based classification method for iceberg and ship discrimination from Sentinel‐1 SAR images with different polarizations and incidence angles. The method is based on the fixed constant false alarm rate (CFAR) detector and the CNN model has three input channels, then the model was trained using parallel algorithm. The CNN is trained using 1443 images and tested using 161 images. The CNN model is also compared with support vector machine (SVM) and k nearest neighbors (kNN) using the same dataset. Comparison shows the CNN‐based method performs the best, and it achieved a validation accuracy of 96%.

This paper describes the design of an efficient parallel implementation of an ice simulator that simulates the behaviour of a ship operating in pack ice. The main idea of the method is to treat ice as a set of discrete objects with very simple properties, and to model the system mechanics mainly as a set of discrete contact and failure events. In this way it becomes possible to parallelize the problem, so that a very large number of ice floes can be modeled. This approach is called the Ice Event Mechanics Modeling (IEMM) method which builds a system solution from a large set of discrete events occurring between a large set of discrete objects. The simulator is developed using the NVIDIA Compute Unified Device Architecture (CUDA). This paper also describes the execution of experiments to evaluate the performance of the simulator and to validate the numerical modeling of ship operations in pack ice. Our results show speed up of 11 times, reducing simulation time for a large ice field (9,801 floes) from over 2 hours to about 12 minutes.

This paper illustrates how Test Oracles and Formal Specifications, with appropriate tool support, can be used with Test-Driven Development (TDD). In TDD, the test code is a formal documentation of the required behavior of the component or system that is being developed, but this documentation is necessarily incomplete and often over-specific. We describe an alternative approach to TDD that is to develop the specification of the required behavior in a formal notation as a part of the TDD process and to generate test oracles from that specification. We present the results of using this approach to develop programs used in a project at the Faculty of Engineering and Applied Science at Memorial University.

Before designing safety- or mission-critical real-time systems, a specification of the required behavior of the system should be produced and reviewed by domain experts. After the system has been implemented, it should be thoroughly tested to ensure that it behaves correctly. This is best done using a monitor, a system that observes the behavior of a target system and reports if that behavior is consistent with the requirements. Such a monitor can be used both as an oracle during testing and as a supervisor during operation. Monitors should be based on the documented requirements of the system. If the target system is required to monitor or control real-valued quantities, then the requirements, which are expressed in terms of the monitored and controlled quantities, will allow a range of behaviors to account for errors and imprecision in observation and control of these quantities. Even if the controlled variables are discrete valued, the requirements must specify the timing tolerance. Because of the limitations of the devices used by the monitor to observe the environmental quantities, there is unavoidable potential for false reports, both negative and positive, This paper discusses design of monitors for real-time systems, and examines the conditions under which a monitor will produce false reports. We describe the conclusions that can be drawn when using a monitor to observe system behavior.

Before designing safety- or mission-critical real-time systems, a specification of the required behaviour of the system should be produced and reviewed by domain experts. After the system has been implemented, it should be thoroughly tested to ensure that it behaves correctly. This is best done using a monitor, a system that observes the behaviour of a target system and reports if that behaviour is consistent with the requirements. Such a monitor can be used both as an oracle during testing and as a supervisor during operation. Monitors should be based on the documented requirements of the system.

The paper illustrates how software can be described precisely using LD-relations, how these descriptions can be presented in a readable manner using tabular notations, and one way such descriptions can be used to test programs. The authors describe an algorithm that can be used to generate a test oracle from program documentation, and present the results of using a tool based on it to help test part of a commercial network management application. The results demonstrate that these methods can be effective at detecting errors and greatly increase the speed and accuracy of test evaluation when compared with manual evaluation. Such oracles can be used for unit testing, in situ testing, constructing self-checking software, and ensuring consistency between code and documentation.

A fundamental assumption of software testing is that there is some mechanism, an oracle, that will determine whether or not the results of a test execution are correct. In practice this is often done by comparing the output, either automatically or manually, to some pre-calculated, presumably correct, output [17]. However, if the program is formally documented it is possible to use the specification to determine the success or failure of a test execution, as in [1], for example. This paper discusses ongoing work to produce a tool that will generate a test oracle from formal program documentation.