Enhancing MBSE Education with Version Control and Automated Feedback
- URL: http://arxiv.org/abs/2409.15294v1
- Date: Wed, 4 Sep 2024 08:12:57 GMT
- Title: Enhancing MBSE Education with Version Control and Automated Feedback
- Authors: Levente Bajczi, Dániel Szekeres, Daniel Siegl, Vince Molnár,
- Abstract summary: This paper presents an innovative approach to conducting a Model-Based Systems Engineering (MBSE) course, engaging over 80 participants annually.
The course is structured around collaborative group assignments, where students utilize Enterprise Architect to complete complex systems engineering tasks across six submissions.
This year, we introduced several technological advancements to enhance the learning experience, including the use of LemonTree, SmartGit, and GitHub.
- Score: 0.10499611180329801
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents an innovative approach to conducting a Model-Based Systems Engineering (MBSE) course, engaging over 80 participants annually. The course is structured around collaborative group assignments, where students utilize Enterprise Architect to complete complex systems engineering tasks across six submissions. This year, we introduced several technological advancements to enhance the learning experience, including the use of LemonTree, SmartGit, and GitHub. Students collaborated on shared repositories in GitHub, received continuous feedback via automated checks through LemonTree Automation, and documented their progress with pre-rendered, continuously updating diagrams. Additionally, they managed 2-way and 3-way merges directly in SmartGit, with merge issues, updates, and model statistics readily available for each Work-in-Progress submission. The process of correcting and providing manual feedback was streamlined, thanks to accessible changelogs and renders in GitHub. An end-of-course feedback form revealed high student satisfaction.
Related papers
- OS-ATLAS: A Foundation Action Model for Generalist GUI Agents [55.37173845836839]
OS-Atlas is a foundational GUI action model that excels at GUI grounding and OOD agentic tasks.
We are releasing the largest open-source cross-platform GUI grounding corpus to date, which contains over 13 million GUI elements.
arXiv Detail & Related papers (2024-10-30T17:10:19Z) - 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) - GitSEED: A Git-backed Automated Assessment Tool for Software Engineering and Programming Education [0.0]
This paper introduces GitSEED, a language-agnostic automated assessment tool designed for Programming Education and Software Engineering (SE)
Using GitSEED, students in Computer Science (CS) and SE can master the fundamentals of git while receiving personalized feedback on their programming assignments and projects.
Our experiments assess GitSEED's efficacy via comprehensive user evaluation, examining the impact of feedback mechanisms and features on student learning outcomes.
arXiv Detail & Related papers (2024-09-11T15:50:42Z) - Visual Analysis of GitHub Issues to Gain Insights [2.9051263101214566]
This paper presents a prototype web application that generates visualizations to offer insights into issue timelines.
It focuses on the lifecycle of issues and depicts vital information to enhance users' understanding of development patterns.
arXiv Detail & Related papers (2024-07-30T15:17:57Z) - How to Understand Whole Software Repository? [64.19431011897515]
An excellent understanding of the whole repository will be the critical path to Automatic Software Engineering (ASE)
We develop a novel method named RepoUnderstander by guiding agents to comprehensively understand the whole repositories.
To better utilize the repository-level knowledge, we guide the agents to summarize, analyze, and plan.
arXiv Detail & Related papers (2024-06-03T15:20:06Z) - Git-Theta: A Git Extension for Collaborative Development of Machine
Learning Models [26.107117592578632]
We introduce Git-Theta, a version control system for machine learning models.
Git-Theta is an extension to Git, the most widely used version control software.
arXiv Detail & Related papers (2023-06-07T15:37:50Z) - SequeL: A Continual Learning Library in PyTorch and JAX [50.33956216274694]
SequeL is a library for Continual Learning that supports both PyTorch and JAX frameworks.
It provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches.
We release SequeL as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.
arXiv Detail & Related papers (2023-04-21T10:00:22Z) - CommitBART: A Large Pre-trained Model for GitHub Commits [8.783518592487248]
We present CommitBART, a large pre-trained encoder-decoder Transformer model for GitHub commits.
The model is pre-trained by three categories (i.e., denoising objectives, cross-modal generation and contrastive learning) for six pre-training tasks to learn commit fragment representations.
Experiments on these tasks demonstrate that CommitBART significantly outperforms previous pre-trained works for code.
arXiv Detail & Related papers (2022-08-17T06:35:57Z) - ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
In this paper, we frame the problem of providing feedback as few-shot classification.
A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors.
Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
arXiv Detail & Related papers (2021-07-23T22:41:28Z) - VEGA: Towards an End-to-End Configurable AutoML Pipeline [101.07003005736719]
VEGA is an efficient and comprehensive AutoML framework that is compatible and optimized for multiple hardware platforms.
VEGA can improve the existing AutoML algorithms and discover new high-performance models against SOTA methods.
arXiv Detail & Related papers (2020-11-03T06:53:53Z) - Student Teamwork on Programming Projects: What can GitHub logs show us? [3.764846583322767]
We collected GitHub logs from two programming projects in two offerings of a CS2 Java programming course for computer science majors.
Students worked in pairs for both projects (one optional, the other mandatory) in each year.
We can identify the students' teamwork style automatically from their submission logs.
arXiv Detail & Related papers (2020-08-25T20:41:52Z)
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.