Community evolution on Stack Overflow

Question and answer (Q&A) websites are a medium where people can communicate and help each other. Stack Overflow is one of the most popular Q&A websites about programming, where millions of developers seek help or provide valuable assistance. Activity on the Stack Overflow website is moderated by the user community, utilizing a voting system to promote high quality content. The website was created on 2008 and has accumulated a large amount of crowd wisdom about the software development industry. Here we analyse this data to examine trends in the grouping of technologies and their users into different sub-communities. In our work we analysed all questions, answers, votes and tags from Stack Overflow between 2008 and 2020. We generated a series of user-technology interaction graphs and applied community detection algorithms to identify the biggest user communities for each year, to examine which technologies those communities incorporate, how they are interconnected and how they evolve through time. The biggest and most persistent communities were related to web development. In general, there is little movement between communities; users tend to either stay within the same community or not acquire any score at all. Community evolution reveals the popularity of different programming languages and frameworks on Stack Overflow over time. These findings give insight into the user community on Stack Overflow and reveal long-term trends on the software development industry.

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Funktionale Programmiersprachen zur Entwicklung numerischer Steuerungen*/Use of functional programming languages for developing computerized numerical controls - Analysis of their potential and fields of application

wt Werkstattstechnik online ◽

10.37544/1436-4980-2017-05-25 ◽

2017 ◽

Vol 107 (05) ◽

pp. 329-333

Author(s):

O. Gerlach ◽

A. Csiszar ◽

A. Lechler ◽

A. Prof. Verl

Keyword(s):

Software Development ◽

Programming Languages ◽

Functional Programming ◽

Functional Programming Languages ◽

Software Complexity ◽

Long Term Trends

Zunehmende Software-Komplexität und parallele Ausführung von Funktionen sind langfristige Trends in der Software-Entwicklung, auch im Bereich numerischer Steuerungen. Dieser Fachbeitrag analysiert, wie funktionale Programmiersprachen dabei helfen können, diese Trends erfolgreich zu handhaben und welche Herausforderungen sich dabei im Speziellen bei numerischen Steuerungen stellen. Es werden Ansätze aufgezeigt, um diesen Herausforderungen zu begegnen.   Increasing software complexity and parallelization of functionality are long-term trends in software development. This also affects the software for numerical controls. This paper analyzes how functional programming languages can help to successfully handle these trends. Challenges that hinder the application of the languages for numerical controls are identified and approaches to meet these challenges are described.

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Knowledge reuse in software projects: Retrieving software development Q&A posts based on project task similarity

PLoS ONE ◽

10.1371/journal.pone.0243852 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243852

Author(s):

Glaucia Melo ◽

Toacy Oliveira ◽

Paulo Alencar ◽

Donald Cowan

Keyword(s):

Software Development ◽

Programming Languages ◽

Knowledge Reuse ◽

Software Projects ◽

Explicit Integration ◽

Stack Overflow ◽

Task Similarity ◽

Development Phases ◽

Amount Of Knowledge ◽

Agile Software

Software developers need to cope with a massive amount of knowledge throughout the typical life cycle of modern projects. This knowledge includes expertise related to the software development phases (e.g., programming, testing) using a wide variety of methods and tools, including development methodologies (e.g., waterfall, agile), software tools (e.g., Eclipse), programming languages (e.g., Java, SQL), and deployment strategies (e.g., Docker, Jenkins). However, there is no explicit integration of these various types of knowledge with software development projects so that developers can avoid having to search over and over for similar and recurrent solutions to tasks and reuse this knowledge. Specifically, Q&A sites such as Stack Overflow are used by developers to share software development knowledge through posts published in several categories, but there is no link between these posts and the tasks developers perform. In this paper, we present an approach that (i) allows developers to associate project tasks with Stack Overflow posts, and (ii) recommends which Stack Overflow posts might be reused based on task similarity. We analyze an industry dataset, which contains project tasks associated with Stack Overflow posts, looking for the similarity of project tasks that reuse a Stack Overflow post. The approach indicates that when a software developer is performing a task, and this task is similar to another task that has been associated with a post, the same post can be recommended to the developer and possibly reused. We believe that this approach can significantly advance the state of the art of software knowledge reuse by supporting novel knowledge-project associations.

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Understanding the Working Habits of GH-SO Users on GitHub Commit Activity and Stack Overflow Post Activity

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194021500467 ◽

2021 ◽

Vol 31 (10) ◽

pp. 1399-1419

Author(s):

Jungil Kim ◽

Eunjoo Lee

Keyword(s):

Software Development ◽

Programming Languages ◽

Exploratory Study ◽

Software Projects ◽

Activity Data ◽

Change Over Time ◽

Stack Overflow ◽

Target Sample ◽

Over Time

GitHub and Stack Overflow are often used together for software development. GH-SO users, who use both GitHub and Stack Overflow, contribute to the development of various software projects in GitHub and share their knowledge and experience on software development in Stack Overflow. To widely understand the interests and working habits of GH-SO users on software development, it is important to investigate how GH-SO users utilize GitHub and Stack Overflow. In this paper, we present an exploratory study on GitHub commit and Stack Overflow post activities of GH-SO users. Specifically, we investigate the working habits of GH-SO users on GitHub commit and Stack Overflow post activities. We randomly selected 19,756 of GH-SO users as our target sample and collected 2,819,483 and 2,147,317 of commit activity data and post activity data of the GH-SO users. We then categorized the collected commit and post activity datasets into specific categories on programming languages and statistically analyzed the categorized commit and post activity datasets. As the results of our analysis, we found the following: (1) The overall commit and post activities of the GH-SO users share some similarity. (2) The commit activities gradually change while the post activities drastically change over time. (3) The commit activities of the GH-SO users are broadly distributed while the post activities are narrowly distributed and the commit activity can be better predictor for post activity. (4) The commit activity of the GH-SO users tends to be performed prior post activity. We believe that our findings can contribute to finding the ways to better support commit and post activities of GitHub and Stack Overflow users.

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