A5076810982- Advanced Software Engineering Methodologies
- Software System Performance and Reliability
- Software Engineering Research
- Service-Oriented Architecture and Web Services
- Software Reliability and Analysis Research
- Software Testing and Debugging Techniques
- Statistical Methods in Clinical Trials
- Advanced Statistical Process Monitoring
- Industrial Vision Systems and Defect Detection
- Reliability and Agreement in Measurement
- Healthcare Technology and Patient Monitoring
- Network Security and Intrusion Detection
- Machine Learning and Data Classification
- Business Process Modeling and Analysis
- Cloud Computing and Resource Management
- Machine Learning in Healthcare
- Advanced Malware Detection Techniques
- Smart Grid Security and Resilience
- Context-Aware Activity Recognition Systems
KU Leuven
2019-2024
Linnaeus University
2022
IMEC
2022
Ghent University
2022
Recently, we have been witnessing a rapid increase in the use of machine learning techniques self-adaptive systems. Machine has used for variety reasons, ranging from model environment system during operation to filtering large sets possible configurations before analyzing them. While body work on systems exists, there is currently no systematic overview this area. Such an important researchers understand state art and direct future research efforts. This article reports results literature...
A/B testing, also referred to as online controlled experimentation or continuous experimentation, is a form of hypothesis testing where two variants piece software are compared in the field from an end user's point view. widely used practice enable data-driven decision making for development. While few studies have explored different facets research on no comprehensive study has been conducted state-of-the-art testing. Such crucial provide systematic overview driving future forward. To...
When a self-adaptive system detects that its adaptation goals may be compromised, it needs to determine how adapt ensure goals. To end, the can analyze possible options for adaptation, i.e., space, and pick best option achieves Such analysis resource time consuming, in particular when rigorous methods are applied. Hence, exhaustively analyzing all infeasible systems with large spaces. This problem is further complicated as typically include uncertainty parameters only resolved at runtime. In...
When a self-adaptive system needs to adapt, it has analyze the possible options for adaptation, i.e., adaptation space. For systems with large spaces, this analysis process can be resource- and time-consuming. One approach tackle problem is using machine learning techniques reduce space only relevant options. However, existing approaches handle threshold goals, while practical often need address also optimization goals. To limitation, we propose two-stage called Deep Learning Adaptation...
Despite considerable research efforts on handling uncertainty in self-adaptive systems, a comprehensive understanding of the precise nature is still lacking. This paper summarises findings 2023 Bertinoro Seminar Uncertainty Self- Adaptive Systems, which aimed at thoroughly investigating notion uncertainty, and outlining open challenges associated with its systems. The seminar discussions were centered around five core topics: (1) agile end-toend uncertainties goal-oriented (2) managing risks...
With the increasing ubiquity and scale of self-adaptive systems, there is a growing need to decentralize functionality that realizes self-adaptation. Our focus on architecture-based systems where one or more functions for monitoring, analyzing, planning, executing are realized by multiple components coordinate with another. While some earlier studies have shed light existing work decentralization currently no clear overview state art in systems. Yet, having precise view decentralized crucial...
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Modern software systems often have to cope with uncertain operation conditions, such as changing workloads or fluctuating interference in a wireless network. To ensure that these meet their goals uncertainties be mitigated. One approach realize this is self-adaptation equips system feedback loop. The loop implements four core functions -- monitor, analyze, plan, and execute share knowledge the form of runtime models. For large number adaptation options, i.e., spaces, deciding which option...
Recently, we have been witnessing an increasing use of machine learning methods in self-adaptive systems. Machine offer a variety cases for supporting self-adaptation, e.g., to keep runtime models up date, reduce large adaptation spaces, or update rules. Yet, since apply essence statistical methods, they may impact on the decisions made by system. Given wide formal approaches provide guarantees systems, it is important investigate applying when such are used. In this paper, study one...
Micro-services are a common architectural approach to software development today. An indispensable tool for evolving micro-service systems is A/B testing. In testing, two variants, A and B, applied in an experimental setting. By measuring the outcome of evaluation criterion, developers can make evidence-based decisions guide evolution their software. Recent studies highlight need enhancing automation when such experiments conducted iterations. To that end, we contribute novel artifact aims...
Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation these uncertainties can jeopardize system goals. Self-adaptation is a common approach to tackle uncertainties. When goals may be compromised, self-adaptive has select best adaptation option reconfigure by analyzing possible options, i.e., space. Yet, large spaces using rigorous methods resource- and time-consuming,...
Internet of Things (IoT) networks consist small devices that use a wireless communication to monitor and possibly control the physical world. A common threat such are jamming attacks, particular type denial service attack. Current research highlights need for design more effective efficient anti-jamming techniques can handle different types attacks in IoT networks. In this paper, we propose DeMiJA, short Detection Mitigation Jamming Attacks IoT, novel approach deal with DeMiJA leverages...
Artifacts support evaluating new research results and help comparing them with the state of art in a field interest. Over past years, several artifacts have been introduced to self-adaptive systems. While these shown their value, it is not clear what extent on problems self-adaptation that are relevant industry. This paper provides set guidelines for aim at supporting industry-relevant selfadaptation. The grounded data obtained from survey practitioners were derived during working sessions...
In A/B testing two variants of a piece software are compared in the field from an end user's point view, enabling data-driven decision making. While widely used practice, no comprehensive study has been conducted on state-of-the-art testing. This paper reports results systematic literature review that analyzed 141 primary studies. The shows main targets algorithms and visual elements. Single classic tests dominating type tests. Stakeholders have three roles design tests: concept designer,...
A/B testing is a common approach used in industry to facilitate innovation through the introduction of new features or modification existing software. Traditionally, tests are administrated manually and conducted sequentially, with each experiment targeting entire population that uses corresponding application. This can be time-consuming costly, particularly when experiments not relevant population. To tackle these problems, we present self-adaptive called AutoPABS, short for Automated...
Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation these uncertainties can jeopardize system goals. Self-adaptation is a common approach to tackle uncertainties. When goals may be compromised, self-adaptive has select best adaptation option reconfigure by analyzing possible options, i.e., space. Yet, large spaces using rigorous methods resource- and time-consuming,...
A/B testing is a common approach used in industry to facilitate innovation through the introduction of new features or modification existing software. Traditionally, tests are conducted sequentially, with each experiment targeting entire population corresponding application. This can be time-consuming and costly, particularly when experiments not relevant population. To tackle these problems, we introduce self-adaptive called AutoPABS, short for Automated Pipelines using Self-adaptation,...
Artifacts support evaluating new research results and help comparing them with the state of art in a field interest. Over past years, several artifacts have been introduced to self-adaptive systems. While these shown their value, it is not clear what extent on problems self-adaptation that are relevant industry. This paper provides set guidelines for aim at supporting industry-relevant self-adaptation. The grounded data obtained from survey practitioners were derived during working sessions...