Feedback about software products is valuable for assuring software quality and identifying ideas for improvement. Frequently, it is gathered from social media platforms or stores with applications and is in textual format. However, there are some social media platforms that contain information in which textual content is combined with pictures or videos. It has not yet been investigated whether feedback expressed in such a way can be useful for requirements engineers. To address this gap, we designed and conducted an empirical study analyzing 576 Instagram posts with pictures concerning 28 different software products. Then, we asked 5 practitioners about the usefulness of the posts. Remarkably, 51% of analyzed posts contained related information. Their textual content conveyed insights about building a community around the product or related to the product-related aspects, its benefits, features, and content, while images complemented the message by providing visual information about UI, user characteristics, content, and context characteristics. Practitioners suggested RE tasks in which such information would be useful. Our study shows that Instagram contains valuable information that might help formulate or improve software requirements. However, there are also several challenges, mainly how to analyze the posts, that open further research directions.

Context and motivation] More and more often software development projects involve participants of diverse nationalities and languages.Thus, software companies tend to use English as their business language.Moreover, to better prepare for future jobs, students consciously choose university courses in English.[Question/problem] As a result there is an increasing number of software engineers who are working or studying in a language which is not their native language.The question arises whether native language has an effect on the quality of natural language requirements.[Principal ideas/results] From the analysis of the requirements formulated by 44 participants of our empirical study, it follows that native language may have a negative effect on requirements quality, e.g., ambiguity, variability, and grammar issues.Furthermore, different native languages might drive to different quality issues.[Contribution] In order to prevent quality issues, our findings might be used by educators to adjust their materials to cater to different language groups, while practitioners might use them to improve their requirements review process.

Non-functional requirements (NFRs) are not easy to elicit and formulate. Technology development and social media platforms made videos one of the primary means to communicate different information. Also, for software products, videos are used to, e.g., validate the idea of products before development-vision videos, to present them-product videos. Thus, a question arises can we use videos to communicate NFRs within Requirements Engineering activities? We designed an empirical study to answer the stated question and conducted an early investigation of 5 vision videos. We found that requirements about interoperability, time behavior, and usability were the most frequently mentioned in vision videos. We identified also several methods of expressing NFRs, e.g., by showing end-user reactions, using captions or narrator. Our study is the first attempt to investigate the research question. It follows from it that videos seem to be a useful means to communicate NFRs. We identified several challenges in using this medium and in analyzing videos that allowed us to suggest areas that can be included in the roadmap for further research in this area.

[Context and motivation] Research on eliciting requirements from a large number of online reviews using automated means has focused on functional aspects. Assuring the quality of an app is vital for its success. This is why user feedback concerning quality issues should be considered as well [Question/problem] But to what extent do online reviews of apps address quality characteristics? And how much potential is there to extract such knowledge through automation? [Principal ideas/results] By tagging online reviews, we found that users mainly write about "usability" and "reliability", but the majority of statements are on a subcharacteristic level, most notably regarding "operability", "adaptability", "fault tolerance", and "interoperability". A set of 16 language patterns regarding "usability" correctly identified 1,528 statements from a large dataset far more efficiently than our manual analysis of a small subset. [Contribution] We found that statements can especially be derived from online reviews about qualities by which users are directly affected, although with some ambiguity. Language patterns can identify statements about qualities with high precision, though the recall is modest at this time. Nevertheless, our results have shown that online reviews are an unused Big Data source for quality requirements.