DG
D.T. Gupta
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The growth of academic publications, heterogeneity of datasets and the absence of a globally accepted organization identifier introduce the challenge of affiliation disambiguation in bibliographic databases. In this paper, we create a baseline using the currently implemented algorithm for author affiliation linkage in Alexandria3k by comparing it to the ground truth. We aim to explore the usage of LLMs (GPT-4) in the Alexandria3k environment to disambiguate author affiliations. The proposed approach extracts the research organization from textual affiliations provided by researchers through their published works and cross-references the organization across the Research Organization Registry. Our process shows promising results and a significant improvement on the existing algorithm in terms of matching rate and identification of multiple affiliations. We discuss the margin of error in LLM results, limitations of the ground truth, and suggest future research directions.
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The growth of academic publications, heterogeneity of datasets and the absence of a globally accepted organization identifier introduce the challenge of affiliation disambiguation in bibliographic databases. In this paper, we create a baseline using the currently implemented algorithm for author affiliation linkage in Alexandria3k by comparing it to the ground truth. We aim to explore the usage of LLMs (GPT-4) in the Alexandria3k environment to disambiguate author affiliations. The proposed approach extracts the research organization from textual affiliations provided by researchers through their published works and cross-references the organization across the Research Organization Registry. Our process shows promising results and a significant improvement on the existing algorithm in terms of matching rate and identification of multiple affiliations. We discuss the margin of error in LLM results, limitations of the ground truth, and suggest future research directions.
This paper aims to unveil and gather testing-related information from Stack Overflow, highlighting it as a valuable resource for practitioners seeking answers and guidance.
The study aims to accumulate knowledge from real-life experiences shared on Stack Overflow and bridge the knowledge gap between industry practices and teaching practices.
The paper explores different types of software testing, popular frameworks, temporal trends of testing-related technologies, controversial opinions, and recommended practices/advice/suggestions from Stack Overflow posts. The methodology involves determining search terms through literature, querying the Stack Exchange API, conducting frequency analysis of words from posts, and manually inspecting threads. Our results show that the most popular frameworks discussed are Selenium, Spring, JMeter, and React. Automated testing and JavaScript frameworks have shown an upward trajectory over the years. The recommendations made by practitioners were categorized based on the broad scope of topics covered. We draw comparisons and parallels with related previous research and discuss the technical limitations faced during the study.
Overall, this paper uncovers valuable insights from Stack Overflow and provides practitioners with the current view on industry practices. ...
The study aims to accumulate knowledge from real-life experiences shared on Stack Overflow and bridge the knowledge gap between industry practices and teaching practices.
The paper explores different types of software testing, popular frameworks, temporal trends of testing-related technologies, controversial opinions, and recommended practices/advice/suggestions from Stack Overflow posts. The methodology involves determining search terms through literature, querying the Stack Exchange API, conducting frequency analysis of words from posts, and manually inspecting threads. Our results show that the most popular frameworks discussed are Selenium, Spring, JMeter, and React. Automated testing and JavaScript frameworks have shown an upward trajectory over the years. The recommendations made by practitioners were categorized based on the broad scope of topics covered. We draw comparisons and parallels with related previous research and discuss the technical limitations faced during the study.
Overall, this paper uncovers valuable insights from Stack Overflow and provides practitioners with the current view on industry practices. ...
This paper aims to unveil and gather testing-related information from Stack Overflow, highlighting it as a valuable resource for practitioners seeking answers and guidance.
The study aims to accumulate knowledge from real-life experiences shared on Stack Overflow and bridge the knowledge gap between industry practices and teaching practices.
The paper explores different types of software testing, popular frameworks, temporal trends of testing-related technologies, controversial opinions, and recommended practices/advice/suggestions from Stack Overflow posts. The methodology involves determining search terms through literature, querying the Stack Exchange API, conducting frequency analysis of words from posts, and manually inspecting threads. Our results show that the most popular frameworks discussed are Selenium, Spring, JMeter, and React. Automated testing and JavaScript frameworks have shown an upward trajectory over the years. The recommendations made by practitioners were categorized based on the broad scope of topics covered. We draw comparisons and parallels with related previous research and discuss the technical limitations faced during the study.
Overall, this paper uncovers valuable insights from Stack Overflow and provides practitioners with the current view on industry practices.
The study aims to accumulate knowledge from real-life experiences shared on Stack Overflow and bridge the knowledge gap between industry practices and teaching practices.
The paper explores different types of software testing, popular frameworks, temporal trends of testing-related technologies, controversial opinions, and recommended practices/advice/suggestions from Stack Overflow posts. The methodology involves determining search terms through literature, querying the Stack Exchange API, conducting frequency analysis of words from posts, and manually inspecting threads. Our results show that the most popular frameworks discussed are Selenium, Spring, JMeter, and React. Automated testing and JavaScript frameworks have shown an upward trajectory over the years. The recommendations made by practitioners were categorized based on the broad scope of topics covered. We draw comparisons and parallels with related previous research and discuss the technical limitations faced during the study.
Overall, this paper uncovers valuable insights from Stack Overflow and provides practitioners with the current view on industry practices.