Skip to main content

Inference Techniques

Combining type inference techniques for semi-automatic UML generation from Pharo code


This paper explores how to reconstruct UML diagrams from dynamically typed languages such as Smalltalk, which do not use explicit type information. This lack of information makes traditional methods for extracting associations difficult. It addresses the need for automated techniques, particularly in legacy software systems, to facilitate their transformation into modern technologies, focusing on Smalltalk as a case study due to its extensive industrial legacy and modern adaptations like Pharo. 

We propose a way to create UML diagrams from Smalltalk code, focusing on using type inference to determine UML associations. For optimal outcomes for large-scale software systems, we recommend combining different type inference methods in an automatic or semi-automatic way.

We addressed the challenge of generating UML models from Pharo, a dynamically typed Smalltalk-based language, with a focus on type inference. Our approach combines both static and dynamic type inference techniques, integrating tools such as RoelTyper, RBRefactoryTyper, J2Inferer, and our custom real-time inferer. This comprehensive method improves type detection accuracy, which is essential for constructing UML associations and refining the representation of object-oriented.

network security, computer networks, data communication, wireless networking, LAN, WAN, VPN, network topology, routing protocols, cybersecurity, firewall protection, cloud networking, IoT connectivity, network infrastructure, bandwidth management, network monitoring, server configuration, IP addressing, network performance, digital communication

#NetworkTechnology, #NetworkSecurity, #DataCommunication, #WirelessNetwork, #LAN, #WAN, #VPN, #Routing, #Cybersecurity, #CloudNetworking, #IoTNetwork, #NetworkInfrastructure, #FirewallProtection, #ServerSetup, #BandwidthControl, #NetworkMonitoring, #IPConfiguration, #DigitalConnectivity, #SmartNetwork, #TechNetwork

International Conference on Network Science and Graph Analytics

Visit: networkscience.researchw.com

Award Nomination: networkscience-conferences.researchw.com/award-nomination/?ecategory=Awards&rcategory=Awardee

For Enquiries: support@researchw.com

Get Connected Here
---------------------------------
---------------------------------
instagram.com/network_science_awards
tumblr.com/emileyvaruni
in.pinterest.com/network_science_awards
networkscienceawards.blogspot.com
youtube.com/@network_science_awards

Comments

Post a Comment

Popular posts from this blog

HealthAIoT: Revolutionizing Smart Healthcare! HealthAIoT combines Artificial Intelligence and the Internet of Things to transform healthcare through real-time monitoring, predictive analytics, and personalized treatment. It enables smarter diagnostics, remote patient care, and proactive health management, enhancing efficiency and outcomes while reducing costs. HealthAIoT is the future of connected, intelligent, and patient-centric healthcare systems. What is HealthAIoT? HealthAIoT is the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) in the healthcare industry. It integrates smart devices, sensors, and wearables with AI-powered software to monitor, diagnose, and manage health conditions in real-time. This fusion is enabling a new era of smart, connected, and intelligent healthcare systems . Key Components IoT Devices in Healthcare Wearables (e.g., smartwatches, fitness trackers) Medical devices (e.g., glucose monitors, heart rate sensors) Rem...
Post a Comment
Read more
Detecting Co-Resident Attacks in 5G Clouds! Detecting co-resident attacks in 5G clouds involves identifying malicious activities where attackers share physical cloud resources with victims to steal data or disrupt services. Techniques like machine learning, behavioral analysis, and resource monitoring help detect unusual patterns, ensuring stronger security and privacy in 5G cloud environments. Detecting Co-Resident Attacks in 5G Clouds In a 5G cloud environment, many different users (including businesses and individuals) share the same physical infrastructure through virtualization technologies like Virtual Machines (VMs) and containers. Co-resident attacks occur when a malicious user manages to place their VM or container on the same physical server as a target. Once co-residency is achieved, attackers can exploit shared resources like CPU caches, memory buses, or network interfaces to gather sensitive information or launch denial-of-service (DoS) attacks. Why are Co-Resident Attack...
Post a Comment
Read more
 How Network Polarization Shapes Our Politics! Network polarization amplifies political divisions by clustering like-minded individuals into echo chambers, where opposing views are rarely encountered. This reinforces biases, reduces dialogue, and deepens ideological rifts. Social media algorithms further intensify this divide, shaping public opinion and influencing political behavior in increasingly polarized and fragmented societies. Network polarization refers to the phenomenon where social networks—both offline and online—become ideologically homogenous, clustering individuals with similar political beliefs together. This segregation leads to the formation of echo chambers , where people are primarily exposed to information that reinforces their existing views and are shielded from opposing perspectives. In political contexts, such polarization has profound consequences: Reinforcement of Biases : When individuals only interact with like-minded peers, their existing beliefs bec...
Post a Comment
Read more