Stefan Karlsson

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 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

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.