Multimodal 3D object detection significantly enhances perception by fusing LiDAR point clouds and RGB images. However, existing methods often fail to adaptively estimate modality confidence under challenging conditions such as heavy occlusion or sparse point clouds, leading to degraded fusion performance. In this paper, we propose GraphFusion, a multimodal framework that integrates cross-modal graph modeling with Bayesian uncertainty-aware fusion for robust 3D object detection. Specifically, a heterogeneous graph driven by geometric and semantic cues aligns 3D points with 2D pixels. A Bayesian attention mechanism then leverages predictive uncertainty to dynamically reweight modalities, prioritizing high-confidence information and enabling noise-resilient and spatially adaptive fusion. The proposed module is highly generalizable and can be seamlessly integrated into existing detectors as a plug-and-play component. Extensive experiments on KITTI and nuScenes demonstrate that GraphFusion achieves significant accuracy improvements with superior robustness and generalization, especially in complex environments.

Tactical Data Link (TDL) is a complex, specialised system that supports the construction of communication applications. To navigate its complexity, model-based system engineering (MBSE), especially Unified Modeling Language (UML)-based modelling, has emerged as the leading approach in developing TDL-specific systems. However, TDL domain experts often find UML modelling notably challenging. That significantly hinders their full engagement in TDL engineering. To bridge this gap, we introduce DSL-MoLab, a tailored framework of DSL-enabled model-based development toolkit that empowers TDL domain experts to engage with the MBSE process of TDL-specific systems straightforwardly. DSL-MoLab encompasses: a domain-specific language (DSL), TDL-DSL, that incorporates TDL-specific concepts and notations fully understood by TDL domain experts; a TDL-DSL Editor that offers both graphical and command-line interfaces for interactive modelling; a UML2DSL Translator and a DSL2UML Translator that jointly facilitate bidirectional translation between UML and DSL models; and a TDL-Code Generator that converts UML models into executable programs leveraging ANTLR for the process. Additionally, DSL-MoLab utilises WebAssembly to support lightweight service deployment, allowing for running on various OS architectures. Applying this framework to a case study within Link 16 demonstrates its effectiveness in enabling TDL domain experts to significantly contribute to engineering TDL systems straightforwardly.

Abstract Context The UML class diagram is commonly used to model functional structures and software code structures in both the preliminary and detailed design stages. And the abstraction level of UML class diagrams is usually higher than that of source code. Usually, there is a lack of trace links between these class diagrams and the source code, which may cause difficulties in understanding the source code, and affect the software evolution and maintenance. Objective The main goal of this article is to establish the trace links between highly abstracted UML class diagrams in the design phase and source code, and eventually help practitioners better understand source code. Method We propose an approach for the automated trace link establishment between UML class diagrams in the design phase and source code. To address the problem of abstraction level gap between them, we extend the UML class diagram by mining the synonymous phrases of class names and deducing the latent missing relationships between classes from multiple design documents. Then we build the trace links with a two‐phase approach including initial construction with fuzzy matching and further optimization by class relationship inference. Results Experiments on five open‐source projects show that the recalls of our approach are over 94%, and the F2‐scores are over 88%, with the gains of 30% to 60% than the four baselines. Conclusion Our work can be a reference for establishing the initial trace links between highly‐abstracted UML class diagrams and source code. Towards the higher abstraction of design diagrams, we extend UML class diagrams with the statistical analysis on multiple design documents. To guarantee the quality of trace links, we design a two‐phase approach by obtaining the “full but not good enough” trace links and filtering the “probably wrong” links. Experiments show that the main techniques of our approach behave as important role for tracing between high‐level class diagrams and source code.

Design models are essential for multiple tasks in software engineering, such as consistency checking, code generation, and design-to-code tracing. Almost all of these works need a semantically analyzable model to represent the software architecture design, e.g., a UML class diagram. Unfortunately, many design models are stored as images and embedded in text-based documentations, impeding the usage and evolution of these models. Thus, identifying the semantic elements of design models from images is important. However, there are lots of design models with different elements in diverse representations, which ask for different approaches for semantic elements extraction. In order to grasp an overview of the commonly used design model types, we conduct a survey on both open-source communities and industry. We find that design model diagrams are usually embedded in documents as pictures (73.72%), and UML class diagrams are the most used type (55.43%). Considering that there are limited studies on automatically recognizing the semantic elements from class diagram images, we propose an approach, which we call ReSECDI. ReSECDI includes our customized design for extracting UML class diagram elements based on image processing technologies. We design a rectangle clustering method for class recognition, to address the challenge that the presentation of classes may vary due to the UML constraints and tools’ styles. We design a polygonal line merging method and double-recognition-approximation method for relationship recognition to deal with the impact of low resolution on the detection. We evaluate the applicability of ReSECDI on 30 images drawn by three popular UML tools and 50 diagrams collected from the open-source communities, and get promising performances. ReSECDI can recognize all types of semantic elements commonly used. It has well applicability and can be used to process the images drawn by the mainstream tools and stored in different resolutions. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Abstract The microservice application programming interface (API) becomes a growing concern in the IT industry, as a result of the increasing usage of microservice architecture style. There exist many successful practices among companies, communities, and so on. In contrast, the related academic research is still at an early stage, where lacks an overview of technologies for the design, implementation and operation of microservice APIs, as well as a general picture of concerns. In this article, we try to fill this gap by eliciting the technologies and concerns on microservice APIs and establishing a microservice API description model, with the intention of aiding researchers to gain an overview of this field and find possible research directions, and helping practitioners to better understand microservice APIs and be aware of the existing approaches for daily work. Twelve academic papers and 38 gray literatures are selected and analyzed following the systematic literature review approach. Besides, we give our observations from this study. For researchers, our findings show the most cared concerns of practitioners, and our description model can be used as a reference for new theories, experiments, and future research dimensions. For practitioners, our study can be used as a guideline for microservices experimentation and a starting point for practice.

In daily software development, inconsistencies between architecture and code inevitably occur with the continuous contribution, even under model-driven development which can trace between design and code. Many methods have been proposed for consistency checking, but most require huge human efforts on establishing the mappings between architectural and code elements. Besides, the multi-layered architecture and code increases the difficulties in inconsistency detection, while existing algorithms do not handle this well. Thus, we propose an improved mapping method for automated consistency check between software architecture and Java implementation, with the premises that initial tracing between architecture and code are established. To be specific, during software evolution, our method can automatically re-establish the mappings between architecture and code using initial tracing information. Then, with detailed inconsistency check rules, we detect the inconsistencies heuristically. Experiments with two projects show our method's high effectiveness with more than 98% of recall and 96% of precision.