[Context & Motivation] Satisficing Explainability, as a Non-Functional Requirement, is posed to be an increasing social demand. [Question/Problem] Exploring how this quality should be amalgamated into software production is a new avenue of research. [Principal ideas/Results] Using the perspective of intentional modeling, we performed a first probe of challenges for the design of a futuristic kitchen. [Contribution] This paper targets challenges for building socially responsible software by exploring the modeling of ffituristic systems from the point of view of Non-Functional Requirements, in particular of Explainability.

The ubiquitous presence of software in the products we use, together with Artificial Intelligence in these products, has led to an increasing need for consumer trust. Consumers often lose faith in products, and the lack of Trust propagates to the companies behind them. This is even more so in mission-critical systems such as autonomous vehicles and clinical support systems. This paper follows grounded theory principles to elicit knowledge related to Trust, Ethics, and Transparency. We approach these qualities as Non-Functional Requirements (NFRs), aiming to build catalogs to subsidize the construction of Socially Responsible Software. The corpus we have used was built on a selected collection of literature on Corporate Social Responsibility, with an emphasis on Business Ethics. Our challenge is how to encode the social perspective knowledge, mainly through the view of Corporate Social Responsibility, on how organizations or institutions achieve trustworthiness. Since our ground perspective is that of NFRs, results are presented by a catalogue of Trust as a Non-Functional Requirement, represented as a Softgoal Interdependency Graph (SIG). The SIG language helps software engineers in understanding alternatives they have to improve Trust in software products.

Self-Driving cars is a fast-growing area of study both in academia and industry. It is part of a broader domain which involves the development of software for Highly Automated Vehicles (HAV) and notions extracted from Artificial Intelligence/Autonomous Systems (AI/AS). There are many challenges that must be overcome to deliver self-driving cars in a manner that is readily accepted by consumers and society. Studies have shown that although many people are comfortable with the idea of AI helping them to operate their houses or schedule appointments, not many people are comfortable with the idea of cars being driven by AI algorithms. At the same time, insurance companies are concerned about vehicle liability issues and how to demonstrate who/what caused an accident. We believe that self-driving cars that demonstrate transparency in their operations will increase consumer trust which is pivotal to its acceptance and will pave the way for its commercialization and daily use. In this work, we investigate how to pursue the elicitation and modeling of transparency as a Non-Functional Requirement (NFR) to produce self-driving cars that are more robust.

Agent orientation appears as a solution for constructing complex systems adapting the requirements identification to cope with agent properties like autonomy, sociability and pro-activeness. Many methodologies were proposed adopting this new paradigm. However, most of them are still in development and therefore need to be adapted. In this work MaSE, an agent-oriented methodology is evaluated. We use an exemplar proposed in 2001 by Yu and Cysneiros [1] to evaluate both agent/goal orientation and object orientation. This evaluation aims at analysing which are the strengths and weaknesses of MaSE through the methodologies questions proposed in the exemplar.

Practice has proven that softgoal interdependency graphs(SIGs) are a very useful tool for recording and reasoning about non-functional requirements [4], [6], [8]. Practical reuse and design of SIGs, however, rely on the capacity of manually identifying reusable structures, interdependencies, and eliminating harmful redundancies. In this paper, we tackle this shortcomings by firstly proposing a frame representation, that allows us to represent SIGs in terms of lattice-like generalization/specialization hierarchies that can be queried. Secondly, we introduce a closeness criteria that, in the absence of perfect matches, will allow querying for similar or ldquogood enoughrdquo approximations. This mechanism is particularly useful in the identification of softgoal interdependencies. Finally, we argue how the proposed strategy can be used to help identify conflicts in SIG graphs.

Usability is becoming increasingly recognized as being an important factor in the acceptance of systems by end users. There is a basic need for systematic approaches to reason, model and analyze usability from the early stages of software development. Furthermore, it is necessary to develop a usable ontology or classification of measurable aspects that can be used to aid in the specification of usability requirements. These ontologies should be represented in a way that facilitates their use as guidelines for the requirements elicitation process. This work builds on review of literature in the area of human-computer interaction and the emerging field of usability engineering in developing a catalog of aspects of usability that can be considered during requirements gathering. This catalogue is used to guide the requirements engineer through alternatives for achieving usability for information systems. The approach uses i* framework, having usability modeled as a special type of goal.

Nonfunctional requirements (NFRs) have been frequently neglected or forgotten in software design. They have been presented as a second or even third class type of requirement, frequently hidden inside notes. We tackle this problem by treating NFRs as first class requirements. We present a process to elicit NFRs, analyze their interdependencies, and trace them to functional conceptual models. We focus our attention on conceptual models expressed using UML (Unified Modeling Language). Extensions to UML are proposed to allow NFRs to be expressed. We show how to integrate NFRs into the class, sequence, and collaboration diagrams. We also show how use cases and scenarios can be adapted to deal with NFRs. This work was used in three case studies and their results suggest that by using our proposal we can improve the quality of the resulting conceptual models.

Usability has been increasingly recognized as an important factor in the acceptance of systems by end users. Usability requirements can be considered to be requirements that capture the usability goals and associated measures for a system under development. In order to ensure usable systems we must ensure identification of appropriate requirements regarding these critical aspects of systems. There is a basic need for systematic approaches to reason, model and analyze usability from the early stages of the software development. Furthermore, it is necessary to develop a usable ontology or classification of measurable aspects of usability that can be used to aid in the specification of usability requirements. These ontologies should be represented in a way that facilitates their use as guidelines for the requirements elicitation process. We build on review of literature in the area of human-computer interaction and of usability engineering in developing a catalog of aspects of usability that can be considered during requirements gathering. This catalogue is used to guide the requirements engineer through alternatives for achieving usability. The approach is based on the use of the i* framework, having usability modeled as a special type of goal.

Although Non-Functional Requirements (NFRs) have been present in many software development methods, they have been presented as a second or even third class type of requirement, frequently hidden inside notes and therefore, frequently neglected or forgotten. Surprisingly, despite the fact that non-functional requirements arc among the most expensive and difficult to deal with there are still few works that focus on NFRs as first class requirements. Although these Works have brought a contribution on how to represent and deal with NFRs, two aspects remain not sufficiently explored: how to elicit NFRs and how to merge these NFRs with conceptual models. Our work aims at filling this gap, proposing a strategy to elicit NFRs and to integrate them into conceptual models We focus our attention on conceptual models expressed using UML, and therefore, we propose extensions to UML such that NFRs can be expressed. More precisely, we will show how to integrate NFRs to the Class, Sequence and Collaboration Diagrams. We will also show how Use Cases and Scenarios can be adapted to deal with NFRs. This work was validated by three case studies and their results suggest that by using our proposal we can improve the quality of UML models.

There are many different approaches to elicit requirements, each having strengths and weaknesses. Hence, some approaches may be more suitable to one domain than another. Moreover, some domains may require these approaches to be carefully applied or even adapted to work efficiently. Health care domain is one of these domains. It is a complex domain with many subtleties, such as political and legal issues that have to be taken into account. This work brings some of the lessons learned in more than six years working with several hospitals and laboratories. Particularly, this paper presents some elicitation techniques that had to be adapted in order to comply with the constraints imposed by several peculiarities intrinsic to this domain. It also points out some special considerations that must be taken into account regardless the method one chooses to elicit requirements.

Complex information systems need conceptual models to deal with aspects beyond the usual entity and activity framework. Recent research has pointed out that conceptual models need to deal with goals, in order to model complex situations that happen in the real world. Our work investigated a new strategy that allows the integration of non-functional requirements (NFR) into data models. Integration is done by representing the NFRs in a data model and investigating how NFR's satisfaction affects the data model. We adopted the widely used ER conceptual model as the data modeling representation schema. In order to start the validation of our strategy we applied it to a real system. The results so far confirm our hypothesis that NFR's integration into data models do help in achieving better understanding of what is being modeled.