Stack Overflow – Informal learning and the global expansion of professional development and opportunities in programming?

The purpose of this study is to examine if, and to what extent, online Community Question Answering platforms expand the opportunities for professional development in programming. Longitudinal and cross-sectional analyses of Stack Overflow Developer Surveys were used to examine users' geographical distribution, gender, experience, professional status, platform usage and education. In order to study differences between the countries with the largest number of respondents, the developer survey data was combined with indicators of human development, gender equality and educational attainment. The results show that the Stack Overflow community has expanded to some extent, both in terms of wider geographical distribution and the programming expertise of users. However, the community reflects and fails to mitigate the apparent gender disparity in the field of programming. Furthermore, participation seems to be conditioned by formal education, especially in developing countries. In general, participation patterns in Stack Overflow seem to be heavily influenced by local conditions in different countries.

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Comprehensive Retrospection of Literature Reported Works of Community Question Answering Systems

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c8769.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 1904-1917

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Community ◽

Question Answering ◽

Hybrid Approach ◽

Open Educational Resources ◽

Stack Overflow ◽

Community Question Answering ◽

Question Answering Systems ◽

Answer Quality

Community question answering CQA) systems are rapidly gaining attention in the society. Several researchers have actively engaged in improving the theories associated with question answering (QA) systems. This paper reviews the literature reported works on question answering QA systems. In this paper, we discuss on the early contributions on QA systems along with their present and future scope. We have categorized the literature reported works into 20 subgroups according to their significance and relevance. The works in each group will be brought out along with their inter-relevance. Finding the question and answer quality is the prime challenge almost addressed by many researchers. Modeling similar questions, identifying experts in prior and understanding seeker satisfaction also considered as potential challenges. Researchers at the most have done experimentations on popular CQAs like Yahoo! Answers, Wiki Answers, Baidu Knows, Brianly, Quora, Pubmed and Stack Overflow respectively. Machine learning, probabilistic modeling, deep learning and hybrid approach of solving show profound significance in addressing various challenges encounter with QA systems. Today the paradigm of CQA systems took the shift by serving as Open Educational Resources to learning community

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Why is Stack Overflow Failing? Preserving Sustainability in Community Question Answering

IEEE Software ◽

10.1109/ms.2016.34 ◽

2016 ◽

Vol 33 (4) ◽

pp. 80-89 ◽

Cited By ~ 23

Author(s):

Ivan Srba ◽

Maria Bielikova

Keyword(s):

Question Answering ◽

Stack Overflow ◽

Community Question Answering

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A Hybrid Fuzzy System via Topic Model for Recommending Highlight Topics of CQA in Developer Communities

Journal of Circuits System and Computers ◽

10.1142/s0218126620502485 ◽

2020 ◽

Vol 29 (15) ◽

pp. 2050248

Author(s):

Hamed Jelodar ◽

Yongli Wang ◽

Ahamdreza Vajdi ◽

Mahdi Rabbani ◽

Ruxin Zhao ◽

...

Keyword(s):

Latent Dirichlet Allocation ◽

Question Answering ◽

Topic Model ◽

Fuzzy Rules ◽

Stack Overflow ◽

Community Question Answering ◽

Semantic Mining ◽

Effective Role ◽

Asking Questions ◽

Programming Knowledge

Question-answering (QA) websites supply a quickly growing source of useful information in numerous areas. These platforms present novel opportunities for online users to supply solutions, they also pose numerous challenges with the ever-growing size of the QA community. QA sites supply platforms for users to cooperate in the form of asking questions or giving answers. Stack Overflow is a massive source of information for both industry and academic practitioners, and its analysis can supply useful insights. Topic modeling of Stack Overflow is very beneficial for pattern discovery and behavior analysis in programming knowledge. In this paper, we propose a framework based on the Latent Dirichlet Allocation (LDA) algorithm and fuzzy rules for question topic mining and recommending highlight latent topics in a community question-answering (CQA) forum of developer community. We consider a real dataset and use 170,091 programmer questions in the R language forum from the Stack Overflow website. Our result shows that LDA topic models via novel fuzzy rules can play an effective role for extracting meaningful concepts and semantic mining in question-answering forums in developer communities.

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