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Abstract: With the growing capabilities of autonomous vehicles, there is a higher demand for sophisticated and pragmatic quality assurance approaches for machine learning-enabled systems in the automotive AI context. The use of simulation-based prototyping platforms provides the possibility for early-stage testing, enabling inexpensive testing and the ability to capture critical corner-case test scenarios. ...
Abstract: Efficient communication is paramount for time-critical applications. Emerging time-critical healthcare applications will require extremely low latency, high reliability, and security guarantees. There are existing and emerging network technologies such as 5G that could enable efficient communications for these time-critical applications. However, it requires detailed identification of the required...
Abstract: In recent years, with the development of computation capability in devices, companies are eager to investigate and utilize suitable ML/DL methods to improve their service quality. However, with the traditional learning strategy, companies need to first build up a powerful data center to collect and analyze data from the edge and then perform centralized model training, which turns out to be ineffi...
Abstract: A/B testing is gaining attention in the automotive sector as a promising tool to measure casual effects from software changes. Different from the web-facing businesses, where A/B testing has been well-established, the automotive domain often suffers from limited eligible users to participate in online experiments. To address this shortcoming, we present a method for designing balanced control and ...
Abstract: Machine learning may enable the automated generation of test oracles. We have characterized emerging research in this area through a systematic literature review examining oracle types, researcher goals, the ML techniques applied, how the generation process was assessed, and the open research challenges in this emerging field. Based on a sample of 22 relevant studies, we observed that ML algorithm...
Abstract: The lack of labeled data is a major problem in both research and industrial settings since obtaining labels is often an expensive and time-consuming activity. In the past years, several machine learning algorithms were developed to assist and perform automated labeling in partially labeled datasets. While many of these algorithms are available in open-source packages, there is a lack of research t...
Abstract: Multimodal Learning Analytics (MMLA) provides opportunities for understanding and supporting collaborative problem-solving. However, the implementation of MMLA systems is challenging due to the lack of scalable technologies and limited solutions for collecting data from group work. This paper proposes the Multimodal Box (MBOX), an IoT-based system for MMLA, allowing the collection and processing o...
Abstract: Continuous Deployment (CD) advocates for quick and frequent deployments of software to production. The goal is to bring new functionality as early as possible to users while learning from their usage. CD has emerged from web-based applications where it has been gaining traction over the past years. While CD is appealing for many software development organizations, empirical evidence on perceived b...
Abstract: The lack of labeled data is a major problem in both research and industrial settings since obtaining labels is often an expensive and time-consuming activity. In the past years, several machine learning algorithms were developed to assist and perform automated labeling in partially labeled datasets. While many of these algorithms are available in open-source packages, there is a lack of research t...
Abstract: A/B experimentation is a known technique for data-driven product development and has demonstrated its value in web-facing businesses. With the digitalisation of the automotive industry, the focus in the industry is shifting towards software. For automotive embedded software to continuously improve, A/B experimentation is considered an important technique. However, the adoption of such a technique ...
Abstract: Test automation is common in software development; often one tests repeatedly to identify regressions. If the amount of test cases is large, one may select a subset and only use the most important test cases. The regression test selection (RTS) could be automated and enhanced with Artificial Intelligence (AI-RTS). This however could introduce ethical challenges. While such challenges in AI are in ...
Abstract: In this paper, we develop and compare the performance of different controllers for balancing an autonomous bicycle. The evaluation is carried out both in simulation, using two different models, and experimentally, on a bicycle instrumented with only lightweight components, and leaving the bicycle structure practically unchanged. Two PID controllers, a Linear Quadratic Regulator (LQR), and a fuzzy ...
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