Advancing model-driven engineering and managing uncertainty in intelligent software systems across modeling, testing, and evolution.
← Back to ResearchAdvancing MDE and uncertainty-aware software engineering.
Our model-driven engineering research develops intelligent approaches for creating, understanding, assessing, and evolving software models.
Our uncertainty-aware software engineering research develops methods for understanding, specifying, modeling, evolving, and testing uncertainties in software and cyber-physical systems.
Selected work spanning model-driven engineering and uncertainty-aware software engineering.
This work introduces PSUM-SysMLv2, an extension that incorporates the OMG Precise Semantics for Uncertainty Modeling metamodel into SysML v2. Seven case studies examine its completeness, expressiveness, and applicability across SysML v2 elements and application domains; the paper also provides an application methodology and two realization approaches.
Venue Software and Systems Modeling
The paper presents TrustModel, a vision for agentic generation and evolution of living knowledge models. Its Modeling, Conformance, and Evolution subsystems construct and update models, assess alignment with systems and environments, and generate evolution guidance, with model-based testing illustrating the approach.
VenuearXiv · Preprint
The paper motivates uncertainty-driven, system-level fuzzing for microservices and identifies challenges in modeling, injecting, propagating, and analyzing uncertainty across services. It outlines a continuous testing architecture combining service virtualization, uncertainty simulation, adaptive test generation, optimization, and causal reasoning for fault localization.
VenueACM Transactions on Software Engineering and Methodology
A systematic literature review of 228 primary studies maps how large language models are used across model-driven engineering tasks. It examines targeted modeling formalisms, prompting and enhancement strategies, evaluation practices, available tools and datasets, and gaps in realistic benchmarking and reproducibility.
VenueZenodo · Preprint
This study examines flakiness in REST API fuzzing through empirical investigations of 36 APIs and an analysis of nearly 3,000 failing tests. Based on the identified sources of instability, it introduces FlakyCatch to detect and mitigate flaky behavior in tests produced by white-box and black-box fuzzers.
VenuearXiv · Preprint
This work investigates how uncertainty originating in autonomous-driving sensor data affects downstream deep-learning models. It focuses on tracing uncertainty from model inputs to learning-component behavior, supporting more dependable perception and uncertainty-aware evaluation of autonomous driving systems.
VenueInformation and Software Technology
This work proposes a domain-specific language for defining multi-level, uncertainty-aware CPS test oracles and an automated generator that packages them as a DevOps-compatible microservice. Evaluation on two industrial systems and nine open-source CPSs assesses requirements coverage and practical generation time.
VenueSoftware and Systems Modeling