A Disruptive Research Playbook for Studying Disruptive Innovations
As researchers today, we are witnessing a fundamental change in our technologically-enabled world due to the advent and diffusion of highly disruptive technologies such as generative Artificial Intelligence (AI), Augmented Reality (AR) and Virtual Reality ...
Speeding Up Genetic Improvement via Regression Test Selection
Genetic Improvement (GI) uses search-based optimisation algorithms to automatically improve software with respect to both functional and non-functional properties. Our previous work showed that Regression Test Selection (RTS) can help speed up the use of ...
Efficient Management of Containers for Software Defined Vehicles
Containerization technology, such as Docker, is gaining in popularity in newly established software-defined vehicle architectures (SDVA). However, executing those containers can quickly become computationally expensive in constrained environments, given ...
MET-MAPF: A Metamorphic Testing Approach for Multi-Agent Path Finding Algorithms
The Multi-Agent Path Finding (MAPF) problem, i.e., the scheduling of multiple agents to reach their destinations, has been widely investigated. Testing MAPF systems is challenging, due to the complexity and variety of scenarios and the agents’ ...
HeMiRCA: Fine-Grained Root Cause Analysis for Microservices with Heterogeneous Data Sources
Microservices architecture improves software scalability, resilience, and agility but also poses significant challenges to system reliability due to their complexity and dynamic nature. Identifying and resolving anomalies promptly is crucial because they ...
Cleaning Up Confounding: Accounting for Endogeneity Using Instrumental Variables and Two-Stage Models
Studies in empirical software engineering are often most useful if they make causal claims because this allows practitioners to identify how they can purposefully influence (rather than only predict) outcomes of interest. Unfortunately, many non-...