What Do Developers Discuss in Their Workplace? An Analysis of Workplace StackExchange Discussions

• URL: http://arxiv.org/abs/2411.07012v1
• Date: Mon, 11 Nov 2024 14:15:40 GMT
• Title: What Do Developers Discuss in Their Workplace? An Analysis of Workplace StackExchange Discussions
• Authors: Natasha Grech, Md Farhad Hossain, Omar Alam,
• Abstract summary: This paper analyzes 47,368 posts on the Workplace StackExchange site. We identified 46 distinct topics grouped into seven categories: Employee Wellness, Communication, Career Movement & Hiring, Conflicts & Mistakes, Corporate Policies, Management/Supervisor responsibilities, and Learning & Technical Skills.
• Score: 1.7532822703595765
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Software workplaces are increasingly recognized as key spaces for professional development, where developers encounter various challenges in their roles, which they often discuss in online forums. This paper analyzes 47,368 posts on the Workplace StackExchange site, aggregating developer insights and applying topic modeling techniques. Through manual analysis, we identified 46 distinct topics grouped into seven categories: Employee Wellness, Communication, Career Movement \& Hiring, Conflicts \& Mistakes, Corporate Policies, Management/Supervisor Responsibilities, and Learning \& Technical Skills. Our findings show that approximately 30\% of discussions involve workplace conflicts, marking this as the most prominent topic. Additionally, we found that workplace culture, harassment, and other corporate policy-related issues represent significant areas of difficulty commonly discussed among developers.

Related papers

• An Empirical Investigation on the Challenges in Scientific Workflow Systems Development [2.704899832646869]
  This study examines interactions between developers and researchers on Stack Overflow (SO) and GitHub. By analyzing issues, we identified 13 topics (e.g., Errors and Bug Fixing, Documentation, Dependencies) and discovered that data structures and operations is the most difficult. We also found common topics between SO and GitHub, such as data structures and operations, task management, and workflow scheduling.
  arXiv  Detail & Related papers  (2024-11-16T21:14:11Z)
• Developer Challenges on Large Language Models: A Study of Stack Overflow and OpenAI Developer Forum Posts [2.704899832646869]
  Large Language Models (LLMs) have gained widespread popularity due to their exceptional capabilities across various domains. This study investigates developers' challenges by analyzing community interactions on Stack Overflow and OpenAI Developer Forum.
  arXiv  Detail & Related papers  (2024-11-16T19:38:27Z)
• The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends [64.99423243200296]
  Conversation Analysis (CA) strives to uncover and analyze critical information from conversation data. In this paper, we perform a thorough review and systematize CA task to summarize the existing related work. We derive four key steps of CA from conversation scene reconstruction, to in-depth attribution analysis, and then to performing targeted training, finally generating conversations.
  arXiv  Detail & Related papers  (2024-09-21T16:52:43Z)
• The Dual-Edged Sword of Technical Debt: Benefits and Issues Analyzed Through Developer Discussions [8.304493605883744]
  Technical debt (TD) has long been one of the key factors influencing the maintainability of software products. This work is to collectively investigate the practitioners' opinions on the various perspectives of TD from a large collection of articles.
  arXiv  Detail & Related papers  (2024-07-30T17:54:36Z)
• Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions [50.545824691484796]
  We identify six themes around the theme challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI) We identify benefits, harms, and future research directions for the four main themes. We discuss the remaining two themes, Artificial Intelligence & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes.
  arXiv  Detail & Related papers  (2024-04-10T16:18:11Z)
• A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond [84.95530356322621]
  This survey presents a systematic review of the advancements in code intelligence. It covers over 50 representative models and their variants, more than 20 categories of tasks, and an extensive coverage of over 680 related works. Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence.
  arXiv  Detail & Related papers  (2024-03-21T08:54:56Z)
• Understanding Fairness in Software Engineering: Insights from Stack Exchange [9.312605205492456]
  This study provides fairness discussions by software practitioners on Stack Exchange sites. We also want to identify the fairness aspects software practitioners talk about the most.
  arXiv  Detail & Related papers  (2024-02-29T11:02:47Z)
• ExpertQA: Expert-Curated Questions and Attributed Answers [51.68314045809179]
  We conduct human evaluation of responses from a few representative systems along various axes of attribution and factuality. We collect expert-curated questions from 484 participants across 32 fields of study, and then ask the same experts to evaluate generated responses to their own questions. The output of our analysis is ExpertQA, a high-quality long-form QA dataset with 2177 questions spanning 32 fields, along with verified answers and attributions for claims in the answers.
  arXiv  Detail & Related papers  (2023-09-14T16:54:34Z)
• Recent Advances in Direct Speech-to-text Translation [58.692782919570845]
  We categorize the existing research work into three directions based on the main challenges -- modeling burden, data scarcity, and application issues. For the challenge of data scarcity, recent work resorts to many sophisticated techniques, such as data augmentation, pre-training, knowledge distillation, and multilingual modeling. We analyze and summarize the application issues, which include real-time, segmentation, named entity, gender bias, and code-switching.
  arXiv  Detail & Related papers  (2023-06-20T16:14:27Z)
• Software Engineers' Questions and Answers on Stack Exchange [0.0]
  We analyze the questions and answers on the Software Engineering Stack Exchange site that encompasses a broader set of areas. We found that the asked questions are most frequently related to database systems, quality assurance, and agile software development. The most attractive topics were career and teamwork problems, and the least attractive ones were network programming and software modeling.
  arXiv  Detail & Related papers  (2023-06-20T13:39:49Z)
• A Study of Knowledge Sharing related to Covid-19 Pandemic in Stack Overflow [69.5231754305538]
  Study of 464 Stack Overflow questions posted mainly in February and March 2020 and leveraging the power of text mining. Findings reveal that indeed this global crisis sparked off an intense and increasing activity in Stack Overflow with most post topics reflecting a strong interest on the analysis of Covid-19 data.
  arXiv  Detail & Related papers  (2020-04-18T08:19:46Z)
• Is 40 the new 60? How popular media portrays the employability of older software developers [78.42660996736939]
  We analyzed popular online articles and related discussions on Hacker News through the lens of employability issues and potential mitigation strategies. We highlight the importance of keeping up-to-date, specializing in certain tasks or technologies, and present role transitions as a way forward for veteran developers.
  arXiv  Detail & Related papers  (2020-04-13T10:00:03Z)

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.