Stefan Karlsson

Andreas Ranftl, Fernando Alonso-Fernandez, Stefan Karlsson et al.

The authors present a novel face tracking approach where optical flow information is incorporated into a modified version of the Viola Jones detection algorithm. In the original algorithm, detection is static, as information from previous frames is not considered. In addition, candidate windows have to pass all stages of the classification cascade, otherwise they are discarded as containing no face. In contrast, the proposed tracker preserves information about the number of classification stages passed by each window. Such information is used to build a likelihood map, which represents the probability of having a face located at that position. Tracking capabilities are provided by extrapolating the position of the likelihood map to the next frame by optical flow computation. The proposed algorithm works in real time on a standard laptop. The system is verified on the Boston Head Tracking Database, showing that the proposed algorithm outperforms the standard Viola Jones detector in terms of detection rate and stability of the output bounding box, as well as including the capability to deal with occlusions. The authors also evaluate two recently published face detectors based on convolutional networks and deformable part models with their algorithm showing a comparable accuracy at a fraction of the computation time.

Leyla Sadighi, Stefan Karlsson, Carlos Natalino et al.

Machine learning (ML) models trained to detect physical-layer threats on one optical fiber system often fail catastrophically when applied to a different system, due to variations in operating wavelength, fiber properties, and network architecture. To overcome this, we propose a Domain Adaptation (DA) framework based on a Variational Autoencoder (VAE) that learns a shared representation capturing event signatures common to both systems while suppressing system-specific differences. The shared encoder is first trained on the combined data from two distinct optical systems: a 21 km O-band dark-fiber testbed (System 1) and a 63.4 km C-band live metro ring (System 2). The encoder is then frozen, and a classifier is trained using labels from an individual system. The proposed approach achieves 95.3% and 73.5% cross-system accuracy when moving from System 1 to System 2 and vice versa, respectively. This corresponds to gains of 83.4% and 51% over a fully supervised Deep Neural Network (DNN) baseline trained on a single system, while preserving intra-system performance.

Stefan Karlsson, Adnan Causevic, Daniel Sundmark

RESTful APIs are an increasingly common way to expose software systems functionality and it is therefore of high interest to find methods to automatically test and verify such APIs. To lower the barrier for industry adoption, such methods needs to be straightforward to use with a low effort. This paper introduces a method to explore the behaviour of a RESTful API. This is done by using automatic property-based tests produced from OpenAPI documents that describe the REST API under test. We describe how this method creates artifacts that can be leveraged both as property-based test generators and as a source of validation for results (i.e., as test oracles). Experimental results, on both industrial and open source services, indicate how this approach is a low effort way of finding real faults. Furthermore, it supports building additional knowledge about the system under test by automatically exposing misalignment of specification and implementation. Since the tests are generated from the OpenAPI document this method automatically evolves test cases as the REST API evolves.

Stefan Karlsson, Adnan Čaušević, Daniel Sundmark

In recent years, GraphQL has become a popular way to expose web APIs. With its raise of adoption in industry, the quality of GraphQL APIs must be also assessed, as with any part of a software system, and preferably in an automated manner. However, there is currently a lack of methods to automatically generate tests to exercise GraphQL APIs. In this paper, we propose a method for automatically producing GraphQL queries to test GraphQL APIs. This is achieved using a property-based approach to create a generator for queries based on the GraphQL schema of the system under test. Our evaluation on a real world software system shows that this approach is both effective, in terms of finding real bugs, and efficient, as a complete schema can be covered in seconds. In addition, we evaluate the fault finding capability of the method when seeding known faults. 73% of the seeded faults where found, with room for improvements with regards to domain specific behavior, a common oracle challenge in automatic test generation.

Stefan Karlsson, Adnan Čaušević, Daniel Sundmark et al.

Automatic testing of mobile applications has been a well-researched area in recent years. However, testing in industry is still a very manual practice, as research results have not been fully transferred and adopted. Considering mobile applications, manual testing has the additional burden of adequate testing posed by a large number of available devices and different configurations, as well as the maintenance and setup of such devices. In this paper, we propose and evaluate the use of a model-based test generation approach, where generated tests are executed on a set of cloud-hosted real mobile devices. By using a model-based approach we generate dynamic, less brittle, and implementation simple test cases. The test execution on multiple real devices with different configurations increase the confidence in the implementation of the system under test. Our evaluation shows that the used approach produces a high coverage of the parts of the application related to user interactions. Nevertheless, the inclusion of external services in test generation is required in order to additionally increase the coverage of the complete application. Furthermore, we present the lessons learned while transferring and implementing this approach in an industrial context and applying it to the real product.

Stefan Karlsson

The adequate testing of stateful software systems is a hard and costly activity. Failures that result from complex stateful interactions can be of high impact, and it can be hard to replicate failures resulting from erroneous stateful interactions. Addressing this problem in an automatic way would save cost and time and increase the quality of software systems in the industry. In this paper, we propose an approach that uses agents to explore software systems with the intention to find faults and gain knowledge.