Although the term 'non-functional requirement' has been in use for more than 20 years, there is still no consensus in the requirements engineering community what non-functional requirements are and how we should elicit, document, and validate them. On the other hand, there is a unanimous consensus that nonfunctional requirements are important and can be critical for the success of a project.This paper surveys the existing definitions of the term, highlights and discusses the problems with the current definitions, and contributes concepts for overcoming these problems.

Abstract-Twitter is one of the most popular social networks. Previous research found that users employ Twitter to communicate about software applications via short messages, commonly referred to as tweets, and that these tweets can be useful for requirements engineering and software evolution. However, due to their large number-in the range of thousands per day for popular applications-a manual analysis is unfeasible.In this work we present ALERTme, an approach to automatically classify, group and rank tweets about software applications. We apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to specific attributes, such as content category, sentiment and number of retweets. We ran our approach on 68,108 collected tweets from three software applications and compared its results against software practitioners' judgement. Our results show that ALERTme is an effective approach for filtering, summarizing and ranking tweets about software applications. ALERTme enables the exploitation of Twitter as a feedback channel for information relevant to software evolution, including end-user requirements.

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Abstract-Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Naïve Bayes for the sub-classification of NFRs.