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Purpose Software developers extensively use stack overflow (SO) for knowledge sharing on software development. Thus, software engineering researchers have started mining the structured/unstructured data present in certain software repositories including the Q&A software developer community SO, with the aim to improve software development. The purpose of this paper is show that how academics/practitioners can get benefit from the valuable user-generated content shared on various online social networks, specifically from Q&A community SO for software development. Design/methodology/approach A comprehensive literature review was conducted and 166 research papers on SO were categorized about software development from the inception of SO till June 2016. Findings Most of the studies revolve around a limited number of software development tasks; approximately 70 percent of the papers used millions of posts data, applied basic machine learning methods, and conducted investigations semi-automatically and quantitative studies. Thus, future research should focus on the overcoming existing identified challenges and gaps. Practical implications The work on SO is classified into two main categories; “SO design and usage” and “SO content applications.” These categories not only give insights to Q&A forum providers about the shortcomings in design and usage of such forums but also provide ways to overcome them in future. It also enables software developers to exploit such forums for the identified under-utilized tasks of software development. Originality/value The study is the first of its kind to explore the work on SO about software development and makes an original contribution by presenting a comprehensive review, design/usage shortcomings of Q&A sites, and future research challenges.

News publishers have decreased disseminating news through conventional newspapers and have migrated to the use of digital means like websites and purpose-built mobile applications. It is observed that news recommendation systems can automatically process lengthy articles and identify similar articles for readers considering predefined criteria. The objectives of the current work are to identify and classify the challenges in news recommendation domain, to identify state-of-the-art approaches and classify on the application domain, to identify datasets used for evaluation and their sources, the evaluation approaches used and to highlight the challenges explicitly addressed. The literature is thoroughly studied over the time span of 2001-2019 and shortlisted 81 related studies, broadly classified into six categories and discussed. The analysis showed that 60% of news recommendation system adopted a hybrid approach, 66% studies little talk about datasets, and addresses a few challenges from a long list of challenges in the news domain. This article is the first in the field to draw a comprehensive big picture of news recommendation and explore different dimensions covered in the studies. The last section presents the future research opportunities that lead to improving the recommendation of news articles in the news domain.

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