Classification of Reverse-Engineered Class Diagram and Forward-Engineered Class Diagram using Machine Learning

• URL: http://arxiv.org/abs/2011.07313v1
• Date: Sat, 14 Nov 2020 14:56:26 GMT
• Title: Classification of Reverse-Engineered Class Diagram and Forward-Engineered Class Diagram using Machine Learning
• Authors: Kaushil Mangaroliya, Het Patel
• Abstract summary: In software industry it is important to know which type of class diagram it is. Which diagram was used in a particular project is an important factor to be known? We propose to solve this problem by using a supervised Machine Learning technique.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: UML Class diagram is very important to visualize the whole software we are working on and helps understand the whole system in the easiest way possible by showing the system classes, its attributes, methods, and relations with other objects. In the real world, there are two types of Class diagram engineers work with namely 1) Forward Engineered Class Diagram (FwCD) which are hand-made as part of the forward-looking development process, and 2). Reverse Engineered Class Diagram (RECD) which are those diagrams that are reverse engineered from the source code. In the software industry while working with new open software projects it is important to know which type of class diagram it is. Which UML diagram was used in a particular project is an important factor to be known? To solve this problem, we propose to build a classifier that can classify a UML diagram into FwCD or RECD. We propose to solve this problem by using a supervised Machine Learning technique. The approach in this involves analyzing the features that are useful in classifying class diagrams. Different Machine Learning models are used in this process and the Random Forest algorithm has proved to be the best out of all. Performance testing was done on 999 Class diagrams.

Related papers

• Systematic teaching of UML and behavioral diagrams [0.0]
  This article discusses the systematic acquisition of skills required for creating diagrams. The more unusual question types are related to images, such as questions about image annotation. All the demonstrated exercises are suitable for both digital and handwritten training or exams.
  arXiv  Detail & Related papers  (2024-10-23T13:18:54Z)
• RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph [63.87660059104077]
  We present RepoGraph, a plug-in module that manages a repository-level structure for modern AI software engineering solutions. RepoGraph substantially boosts the performance of all systems, leading to a new state-of-the-art among open-source frameworks.
  arXiv  Detail & Related papers  (2024-10-03T05:45:26Z)
• Masked Image Modeling: A Survey [73.21154550957898]
  Masked image modeling emerged as a powerful self-supervised learning technique in computer vision. We construct a taxonomy and review the most prominent papers in recent years. We aggregate the performance results of various masked image modeling methods on the most popular datasets.
  arXiv  Detail & Related papers  (2024-08-13T07:27:02Z)
• MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine [85.80851893886161]
  We propose MAVIS, a MAthematical VISual instruction tuning pipeline for MLLMs, featuring an automatic data engine to efficiently create mathematical visual datasets. We use MAVIS-Caption to fine-tune a math-specific vision encoder (CLIP-Math) through contrastive learning, tailored for improved diagram visual encoding. Third, we adopt MAVIS-Instruct to perform the instruction tuning for robust problem-solving skills, and term the resulting model as MAVIS-7B.
  arXiv  Detail & Related papers  (2024-07-11T17:59:47Z)
• Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation [0.5789654849162464]
  GPT-4-Vision is a state-of-the-art deep learning model. It can transform Unified Modeling Language (UML) class diagrams into fully operating Java class files.
  arXiv  Detail & Related papers  (2024-04-22T17:21:24Z)
• From Image to UML: First Results of Image Based UML Diagram Generation Using LLMs [1.961305559606562]
  In software engineering processes, systems are first specified using a modeling language. Large Language Models (LLM) are used to generate the formal representation of (UML) models from a given drawing. More specifically, we have evaluated the capabilities of different LLMs to convert images of class diagrams into the actual models represented in the images.
  arXiv  Detail & Related papers  (2024-04-17T13:33:11Z)
• CogCoM: Train Large Vision-Language Models Diving into Details through Chain of Manipulations [61.21923643289266]
  Chain of Manipulations is a mechanism that enables Vision-Language Models to solve problems step-by-step with evidence. After training, models can solve various visual problems by eliciting intrinsic manipulations (e.g., grounding, zoom in) actively without involving external tools. Our trained model, textbfCogCoM, achieves state-of-the-art performance across 9 benchmarks from 4 categories.
  arXiv  Detail & Related papers  (2024-02-06T18:43:48Z)
• DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning [62.51232333352754]
  Text-to-image (T2I) generation has seen significant growth over the past few years. Despite this, there has been little work on generating diagrams with T2I models. We present DiagrammerGPT, a novel two-stage text-to-diagram generation framework. We show that our framework produces more accurate diagrams, outperforming existing T2I models.
  arXiv  Detail & Related papers  (2023-10-18T17:37:10Z)
• Curriculum Graph Machine Learning: A Survey [51.89783017927647]
  curriculum graph machine learning (Graph CL) integrates the strength of graph machine learning and curriculum learning. This paper comprehensively overview approaches on Graph CL and present a detailed survey of recent advances in this direction.
  arXiv  Detail & Related papers  (2023-02-06T16:59:25Z)
• An Algebraic Framework for Stock & Flow Diagrams and Dynamical Systems Using Category Theory [2.030738254233949]
  In this chapter, rather than focusing on the underlying mathematics, we informally use communicable disease examples created by the implemented software of StockFlow.jl. We first characterize categorical stock & flow diagrams, and note the clear separation between the syntax of stock & flow diagrams and their semantics. Applying category theory, these frameworks can build large diagrams from smaller ones in a modular fashion.
  arXiv  Detail & Related papers  (2022-11-01T16:15:54Z)
• Automated Diagram Generation to Build Understanding and Usability [0.0]
  Causal loop and stock and flow diagrams are broadly used in System Dynamics because they help organize relationships and convey meaning. This paper demonstrates how that information can be clearly presented in an automatically generated causal loop diagram.
  arXiv  Detail & Related papers  (2020-05-27T22:32:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.