Teaching Simulation as a Research Method in Empirical Software Engineering
- URL: http://arxiv.org/abs/2501.04798v1
- Date: Wed, 08 Jan 2025 19:24:05 GMT
- Title: Teaching Simulation as a Research Method in Empirical Software Engineering
- Authors: Breno Bernard Nicolau de França, Dietmar Pfahl, Valdemar Vicente Graciano Neto, Nauman bin Ali,
- Abstract summary: The chapter supports educators and postgraduate students in understanding the role of simulation in software engineering research.
For educators, it provides learning objectives when teaching simulation, considering the current state of the art in software engineering research.
For students, it drives the learning path for those interested in learning this method but had no opportunity to engage in an entire course on simulation in the context of empirical research.
- Score: 2.517406775855265
- License:
- Abstract: The chapter supports educators and postgraduate students in understanding the role of simulation in software engineering research based on the authors' experience. This way, it includes a background positioning simulation-based studies in software engineering research, the proposition of learning objectives for teaching simulation as a research method, and presents our experience when teaching simulation concepts and practice. For educators, it further provides learning objectives when teaching simulation, considering the current state of the art in software engineering research and the necessary guidance and recommended learning activities to achieve these objectives. For students, it drives the learning path for those interested in learning this method but had no opportunity to engage in an entire course on simulation in the context of empirical research.
Related papers
- Assessing Simulation Knowledge and Proficiency Among Undergraduate Computing Students in Brazil: Insights and Results from a Survey Research [49.98902697476149]
This report highlights the importance of academic training in a dynamic technological environment.
It explores students' perceptions, the tools used, the challenges faced, and the prospects for deeper study.
arXiv Detail & Related papers (2025-02-19T19:56:17Z) - Bringing active learning, experimentation, and student-created videos in engineering: A study about teaching electronics and physical computing integrating online and mobile learning [0.0]
The main aim of this study was to create an AL methodology to learn electronics, physical computing (PhyC), programming, and basic robotics in engineering through hands-on activities and active experimentation in online environments.
The methodology was conceived using the guidelines of the Integrated Course Design Model (ICDM) and in some courses combining mobile and online learning with an Android app.
The outcomes indicate a good perception of the PhyC and programming activities by the students and suggest that these influence motivation, self-efficacy, reduction of anxiety, and improvement of academic performance in the courses.
arXiv Detail & Related papers (2024-06-02T23:26:27Z) - Language Evolution with Deep Learning [49.879239655532324]
Computational modeling plays an essential role in the study of language emergence.
It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language.
This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models.
arXiv Detail & Related papers (2024-03-18T16:52:54Z) - A Game-Based Learning Application to Help Learners to Practice
Mathematical Patterns and Structures [0.0]
The purpose of this study is to develop a game-based mobile application to help learners practice mathematical patterns and structures.
An instrument based on the Octalysis framework was developed as an evaluation tool for the study.
arXiv Detail & Related papers (2023-06-22T13:15:12Z) - Synthetic Data-Based Simulators for Recommender Systems: A Survey [55.60116686945561]
This survey aims at providing a comprehensive overview of the recent trends in the field of modeling and simulation.
We start with the motivation behind the development of frameworks implementing the simulations -- simulators.
We provide a new consistent classification of existing simulators based on their functionality, approbation, and industrial effectiveness.
arXiv Detail & Related papers (2022-06-22T19:33:21Z) - Simulation Intelligence: Towards a New Generation of Scientific Methods [81.75565391122751]
"Nine Motifs of Simulation Intelligence" is a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system.
We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery.
arXiv Detail & Related papers (2021-12-06T18:45:31Z) - Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches.
For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models.
The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
arXiv Detail & Related papers (2021-06-05T01:31:51Z) - A User's Guide to Calibrating Robotics Simulators [54.85241102329546]
This paper proposes a set of benchmarks and a framework for the study of various algorithms aimed to transfer models and policies learnt in simulation to the real world.
We conduct experiments on a wide range of well known simulated environments to characterize and offer insights into the performance of different algorithms.
Our analysis can be useful for practitioners working in this area and can help make informed choices about the behavior and main properties of sim-to-real algorithms.
arXiv Detail & Related papers (2020-11-17T22:24:26Z) - Integrating Machine Learning with HPC-driven Simulations for Enhanced
Student Learning [0.0]
We develop a web application that supports both HPC-driven simulation and the ML surrogate methods to produce simulation outputs.
The evaluation of the tool via in-classroom student feedback and surveys shows that the ML-enhanced tool provides a dynamic and responsive simulation environment.
arXiv Detail & Related papers (2020-08-24T22:48:21Z)
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.