The purpose of requirements engineering (RE) is to make sure that the expectations and needs of the stakeholders of a software system are met. Emotional needs can be captured as emotional requirements that represent how the end user should feel when using the system. Differently from functional and quality (non-functional) requirements, emotional requirements have received relatively less attention from the RE community. This study is motivated by the need to explore and map the literature on emotional requirements. The study applies the systematic mapping study technique for surveying and analyzing the available literature to identify the most relevant publications on emotional requirements. We identified 34 publications that address a wide spectrum of practices concerned with engineering emotional requirements. The identified publications were analyzed with respect to the application domains, instruments used for eliciting and artefacts used for representing emotional requirements, and the state of the practice in emotion-related requirements engineering. This analysis serves to identify research gaps and research directions in engineering emotional requirements. To the best of the knowledge by the authors, no other similar study has been conducted on emotional requirements.

Mining app stores and social media has proven to be a good source for collecting user feedback to foster requirements engineering and software evolution. Recent literature on mining software-related data from social platforms, such as Twitter and Facebook, shows that it complements app store mining. However, there are many other platforms where users discuss and provide feedback on software applications that are not thoroughly researched and analysed. One of such platforms is reddit. In this paper, we introduce reddit as a new potential data source and explore if and how requirements engineering and software evolution can benefit from obtaining user feedback from reddit. We also present an exploratory study in which we analysed the usage characteristics (i.e., frequency of posts, number of comments, and number of users for each subreddit) of reddit posts about software applications. Furthermore, we examined the content of the posts and the results reveal that almost 54% of posts contain useful information. Finally, we investigated the potential of automatic classification and applied machine learning algorithms to unstructured and noisy reddit data to perform automated classification into the categories of bug reports, feature related, and irrelevant. We found that the Support Vector Machine algorithm with the F1-score of 84% can be effective in categorizing reddit posts. Our results show that reddit posts provide useful feedback on software applications that can foster requirements engineering and software evolution.

Machine learning (ML) has demonstrated practical impact in a variety of application domains. Software engineering is a fertile domain where ML is helping in automating different tasks. In this paper, our focus is the intersection of software requirement engineering (RE) and ML. To obtain an overview of how ML is helping RE and the research trends in this area, we have surveyed a large number of research articles. We found that the impact of ML can be observed in requirement elicitation, analysis and specification, validation and management. Furthermore, in these categories, we discuss the specific problem solved by ML, the features and ML algorithms used as well as datasets, when available. We outline lessons learned and envision possible future directions for the domain.