Who's actually being Studied? A Call for Population Analysis in Software Engineering Research
- URL: http://arxiv.org/abs/2404.15093v1
- Date: Tue, 23 Apr 2024 14:48:11 GMT
- Title: Who's actually being Studied? A Call for Population Analysis in Software Engineering Research
- Authors: Jefferson Seide Molléri,
- Abstract summary: Population analysis is crucial for ensuring that empirical software engineering (ESE) research is representative and its findings are valid.
We explore the challenges ranging from analysing populations of individual software engineers to organizations and projects.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Population analysis is crucial for ensuring that empirical software engineering (ESE) research is representative and its findings are valid. Yet, there is a persistent gap between sampling processes and the holistic examination of populations, which this position paper addresses. We explore the challenges ranging from analysing populations of individual software engineers to organizations and projects. We discuss the interplay between generalizability and transferability and advocate for appropriate population frames. We also present a compelling case for improved population analysis aiming to enhance the empirical rigor and external validity of ESE research.
Related papers
- Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets [23.462552062769426]
We introduce a computational approach to adaptively allocate recruitment resources among sites to improve representativeness.
In simulated recruitment of 10,000-participant cohorts from medical centers, we show that our approach yields a more representative cohort than existing baselines.
arXiv Detail & Related papers (2024-08-02T16:32:30Z) - Data-Centric AI in the Age of Large Language Models [51.20451986068925]
This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs)
We make the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs.
We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization.
arXiv Detail & Related papers (2024-06-20T16:34:07Z) - SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge [63.311045291016555]
Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts.
This paper summarizes the challenging task, data, and research progress.
arXiv Detail & Related papers (2024-05-17T02:36:14Z) - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
ResearchAgent is a large language model-powered research idea writing agent.
It generates problems, methods, and experiment designs while iteratively refining them based on scientific literature.
We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
arXiv Detail & Related papers (2024-04-11T13:36:29Z) - SurveyAgent: A Conversational System for Personalized and Efficient Research Survey [50.04283471107001]
This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers.
SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level.
Our evaluation demonstrates SurveyAgent's effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.
arXiv Detail & Related papers (2024-04-09T15:01:51Z) - Insights Towards Better Case Study Reporting in Software Engineering [0.0]
This paper aims to share insights that can enhance the quality and impact of case study reporting.
We emphasize the need to follow established guidelines, accurate classification, and detailed context descriptions in case studies.
We aim to encourage researchers to adopt more rigorous and communicative strategies, ensuring that case studies are methodologically sound.
arXiv Detail & Related papers (2024-02-13T12:29:26Z) - The ethical ambiguity of AI data enrichment: Measuring gaps in research
ethics norms and practices [2.28438857884398]
This study explores how, and to what extent, comparable research ethics requirements and norms have developed for AI research and data enrichment.
Leading AI venues have begun to establish protocols for human data collection, but these are are inconsistently followed by authors.
arXiv Detail & Related papers (2023-06-01T16:12:55Z) - How WEIRD is Usable Privacy and Security Research? (Extended Version) [7.669758543344074]
We conducted a literature review to understand the extent to which participant samples in UPS papers were from WEIRD countries.
Geographic and linguistic barriers in the study methods and recruitment methods may cause researchers to conduct user studies locally.
arXiv Detail & Related papers (2023-05-08T19:21:18Z) - Towards Automated Process Planning and Mining [77.34726150561087]
We present a research project in which researchers from the AI and BPM field work jointly together.
We discuss the overall research problem, the relevant fields of research and our overall research framework to automatically derive process models.
arXiv Detail & Related papers (2022-08-18T16:41:22Z) - Contemporary Research Trends in Response Robotics [0.0]
This paper analyzes the technical content, statistics, and implications of the literature from bibliometric standpoints.
The aim is to study the global progress of response robotics research and identify the contemporary trends.
arXiv Detail & Related papers (2021-04-28T05:35:45Z) - Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations.
Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
arXiv Detail & Related papers (2021-01-15T10:43:10Z)
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.