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.

When performed on a model, a set of operations (e.g., queries or model transformations) rarely uses all the information present in the model. Unintended underuse of a model can indicate various problems: the model may contain more detail than necessary or the operations may be immature or erroneous. Analyzing the footprints of the operations -i.e., the part of a model actually used by an operation -is a simple technique to diagnose and analyze such problems. However, precisely calculating the footprint of an operation is expensive, because it requires analyzing the operation's execution trace.In this paper, we present an automated technique to estimate the footprint of an operation without executing it. We evaluate our approach by applying it to 75 models and five operations. Our technique provides software engineers with an efficient, yet precise, evaluation of the usage of their models. ABSTRACTWhen performed on a model, a set of operations (e.g., queries or model transformations) rarely uses all the information present in the model. Unintended underuse of a model can indicate various problems: the model may contain more detail than necessary or the operations may be immature or erroneous. Analyzing the footprints of the operationsi.e., the part of a model actually used by an operation -is a simple technique to diagnose and analyze such problems. However, precisely calculating the footprint of an operation is expensive, because it requires analyzing the operation's execution trace.In this paper, we present an automated technique to estimate the footprint of an operation without executing it. We evaluate our approach by applying it to 75 models and five operations. Our technique provides software engineers with an efficient, yet precise, evaluation of the usage of their models.

Shared understanding is essential for efficient software engineering when the risk of unsatisfactory outcome and rework of project results shall be low. Today, however, shared understanding is used mostly in an unreflected, ad-hoc way. This affects the quality of the engineered software solutions and generates re-work once the quality problems are discovered. In this article, we investigate the role, value, and usage of shared understanding in software engineering. We contribute a reflected analysis of the problem, in particular of how to rely on shared understanding that is implicit, rather than explicit. After an overview of the state of the art we discuss forms and value of shared understanding in software engineering, survey enablers and obstacles, compile existing practices for dealing with shared understanding, and present a roadmap for improving knowledge and practice in this area. Abstract Shared understanding is essential for efficient software engineering when the risk of unsatisfactory outcome and rework of project results shall be low. Today, however, shared understanding is used mostly in an unreflected, ad-hoc way. This affects the quality of the engineered software solutions and generates re-work once the quality problems are discovered. In this article, we investigate the role, value, and usage of shared understanding in software engineering. We contribute a reflected analysis of the problem, in particular of how to rely on shared understanding that is implicit, rather than explicit. After an overview of the state of the art we discuss forms and value of shared understanding in software engineering, survey enablers and obstacles, compile existing practices for dealing with shared understanding, and present a roadmap for improving knowledge and practice in this area.

Although most software engineers have access to various modeling tools, they often use paper and pencil to sketch ideas and to support modeling activities. This is particularly true when they are working in the field, for example gathering requirements from stakeholders. Sketches documented on paper very often need to be re-modeled in order to allow further processing -an error-prone and timeconsuming task. The aim of our work is to better integrate these early sketching and modeling activities into the overall software engineering process. We have prototyped FlexiSketch, a mobile application that supports freeform, flexible, in-situ modeling and allows software engineers to annotate their models. Apart from the application and the underlying conceptual solution we also present the results of initial experiments. Those suggest that the tool supports freeform sketching similar to paper and pencil, and that practitioners would be willing to use a tool like FlexiSketch in their daily work. Abstract. Although most software engineers have access to various modeling tools, they often use paper and pencil to sketch ideas and to support modeling activities. This is particularly true when they are working in the field, for example gathering requirements from stakeholders. Sketches documented on paper very often need to be re-modeled in order to allow further processing -an error-prone and time-consuming task. The aim of our work is to better integrate these early sketching and modeling activities into the overall software engineering process. We have prototyped FlexiSketch, a mobile application that supports freeform, flexible, in-situ modeling and allows software engineers to annotate their models. Apart from the application and the underlying conceptual solution we also present the results of initial experiments. Those suggest that the tool supports freeform sketching similar to paper and pencil, and that practitioners would be willing to use a tool like FlexiSketch in their daily work.