Teaching Empirical Research Methods in Software Engineering: An Editorial Introduction
- URL: http://arxiv.org/abs/2501.07195v1
- Date: Mon, 13 Jan 2025 10:42:43 GMT
- Title: Teaching Empirical Research Methods in Software Engineering: An Editorial Introduction
- Authors: Daniel Mendez, Paris Avgeriou, Marcos Kalinowski, Nauman bin Ali,
- Abstract summary: Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering.
While extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering.
- Score: 2.518416353853374
- License:
- Abstract: Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.
Related papers
- Estimating the Energy Footprint of Software Systems: a Primer [56.200335252600354]
quantifying the energy footprint of a software system is one of the most basic activities.
This document aims to be a starting point for researchers who want to begin conducting work in this area.
arXiv Detail & Related papers (2024-07-16T11:21:30Z) - Integrating Human-Centric Approaches into Undergraduate Software Engineering Education: A Scoping Review and Curriculum Analysis in the Australian Context [0.0]
Human-Centric Software Engineering refers to the software engineering processes that put human needs and requirements as core practice.
A large majority of software projects fail to cater to human needs and consequently run into budget, delivery, and usability issues.
This paper presents a scoping review to identify the topics and curriculum approaches suitable for teaching HCSE to undergraduate software engineering students.
arXiv Detail & Related papers (2024-07-10T02:34:58Z) - Ten Years of Teaching Empirical Software Engineering in the context of Energy-efficient Software [12.26887943861433]
We share our experience in running ten editions of the Green Lab course at the Vrije Universiteit Amsterdam, the Netherlands.
The course is given in the Software Engineering and Green IT track of the Computer Science Master program of the VU.
arXiv Detail & Related papers (2024-07-08T07:44:49Z) - Teaching and Learning Ethnography for Software Engineering Contexts [1.0992151305603264]
This chapter provides an introduction to teaching and learning ethnography for faculty teaching ethnography to software engineering graduate students.
The contents of the chapter focus on what we think is the core basic knowledge for newbies to ethnography as a research method.
The chapter is designed to support part of a course on empirical software engineering and provides pointers and literature for further reading.
arXiv Detail & Related papers (2024-07-05T15:43:02Z) - A Systematic Literature Review on the Use of Machine Learning in Software Engineering [0.0]
The study was carried out following the objective and the research questions to explore the current state of the art in applying machine learning techniques in software engineering processes.
The review identifies the key areas within software engineering where ML has been applied, including software quality assurance, software maintenance, software comprehension, and software documentation.
arXiv Detail & Related papers (2024-06-19T23:04:27Z) - MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows [58.56005277371235]
We introduce MASSW, a comprehensive text dataset on Multi-Aspect Summarization of ScientificAspects.
MASSW includes more than 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years.
We demonstrate the utility of MASSW through multiple novel machine-learning tasks that can be benchmarked using this new dataset.
arXiv Detail & Related papers (2024-06-10T15:19:09Z) - Charting a Path to Efficient Onboarding: The Role of Software
Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools.
This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z) - Lessons from Formally Verified Deployed Software Systems (Extended version) [65.69802414600832]
This article examines a range of projects, in various application areas, that have produced formally verified systems and deployed them for actual use.
It considers the technologies used, the form of verification applied, the results obtained, and the lessons that the software industry should draw regarding its ability to benefit from formal verification techniques and tools.
arXiv Detail & Related papers (2023-01-05T18:18:46Z) - Revise and Resubmit: An Intertextual Model of Text-based Collaboration
in Peer Review [52.359007622096684]
Peer review is a key component of the publishing process in most fields of science.
Existing NLP studies focus on the analysis of individual texts.
editorial assistance often requires modeling interactions between pairs of texts.
arXiv Detail & Related papers (2022-04-22T16:39:38Z) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z)
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.