Testing and verification of self-driving cars are essential for ensuring their safety and reliability. In the context of the ICST 2024 self-driving cars testing tool competition, we present ITS4SDC, our tool for selecting roads that challenge lane-keeping assist systems by leading the car off the road. ITS4SDC leverages a long short-term memory-based model for the classification of roads as safe and unsafe and subsequently selects unsafe roads for testing.

ABSTRACT Background High‐level system testing of applications that use data from e‐Government services as input requires test data that is real‐life‐like but where the privacy of personal information is guaranteed. Applications with such strong requirement include information exchange between countries, medicine, banking, and so on. This review aims to synthesise the current state‐of‐the‐practice in this domain. Objectives The objective of this Systematic Review is to identify existing approaches for creating and evolving synthetic test data without using real‐life raw data. Methods We followed well‐known methodologies for conducting systematic literature reviews, including the ones from Kitchenham and PRISMA as well as guidelines for analysing the limitations of our review and its threats to validity. Results A variety of methods and tools exist for creating privacy‐preserving test data. Our search found 1013 publications in IEEE Xplore, ACM Digital Library, and SCOPUS. We extracted data from 75 of those publications and identified 37 approaches that answer our research question partly. A common prerequisite for using these methods and tools is direct access to real‐life data for data anonymization or synthetic test data generation. Nine existing synthetic test data generation approaches were identified that were closest to answering our research question. Nevertheless, further work would be needed to add the ability to evolve synthetic test data to the existing approaches. Conclusions None of the publications covered our requirements completely, only partially. Synthetic test data evolution is a field that has not received much attention from researchers but needs to be explored in Digital Government Solutions, especially since new legal regulations are being put in force in many countries.

The quality of automatic app feature extraction from app reviews depends on various aspects, e.g. the feature extraction method, training and evaluation datasets, evaluation method etc. Annotation guidelines used to guide the annotation of training and evaluation datasets can have a considerable impact to the quality of the whole system but it is one of the aspects that is often overlooked. We conducted a study in which we explore the effects of annotation guidelines to the quality of app feature extraction. We propose several changes to the existing annotation guidelines with the goal of making the extracted app features more useful to app developers. We test the proposed changes via simulating the application of the new annotation guidelines and evaluating the performance of the supervised machine learning models trained on datasets annotated with initial and simulated annotation guidelines. While the overall performance of automatic app feature extraction remains the same as compared to the model trained on the dataset with initial annotations, the features extracted by the model trained on the dataset with simulated new annotations are less noisy and more informative to app developers.