What If People Learn Requirements Over Time? A Rough Introduction to Requirements Economics

The overall objective of Requirements Engineering is to specify, in a systematic way, a system that satisfies the expectations of its stakeholders. Despite tremendous effort in the field, recent studies demonstrate this is objective is not always achieved. In this paper, we discuss one particularly challenging factor to Requirements Engineering projects, namely the change of requirements. We proposes a rough discussion of how learning and time explain requirements changes, how it can be introduced as a key variable in the formulation of the Requirements Engineering Problem, and how this induces costs for a requirements engineering project. This leads to a new discipline of requirements economics

Using ontologies to support and critique decisions

Supporting decision making in the working environment has long being pursued by practitioners across a variety of fields, ranging from sociology and operational research to cognitive and computer scientists. A number of computer-supported systems and various technologies have been used over the years, but as we move into more global and flexible organisational structures, new technologies and challenges arise. In this paper, I argue for an ontology-based solution and present some of the early prototypes we have been developing, assess their impact on the decision making process and elaborate on the costs involved

The Logic of Requirements

Requirements consist of (a) domain assumptions, (b) hard goals, (c) quality constraints, (d) possibly prioritized preferences. The very core of Requirements Engineering consists of the following problem: given a set of (a)-(d), generate specifications that fulfill hard goals and quality constraints, assuming that domain assumptions hold, and satisfy maximal sets of preferences. We are working towards tools that solve this problem for expressive modeling languages in terms of which one can represent domain assumptions, goals, etc. Such tools can be used as basis for exploring requirements by varying preferences and priorities, or weakening/strengthening goals

Obtaining Formal Models through Non-Monotonic Refinement

When designing a model for formal verification, we want to\ud be certain that what we proved about the model also holds for the system we modelled. This raises the question of whether our model represents the system, and what makes us confident about this. By performing so called, non-monotonic refinement in the modelling process, we make the steps and decisions explicit. This helps us to (1) increase the confidence that the model represents the system, (2) structure and organize the communication with domain experts and the problem owner, and (3) identify rational steps made while modelling. We focus on embedded control systems

Arguing security: validating security requirements using structured argumentation

This paper proposes using both formal and structured informal arguments to show that an eventual realized system can satisfy its security requirements. These arguments, called 'satisfaction arguments', consist of two parts: a formal argument based upon claims about domain properties, and a set of informal arguments that justify the claims. Building on our earlier work on trust assumptions and security requirements, we show how using satisfaction arguments assists in clarifying how a system satisfies its security requirements, in the process identifying those properties of domains that are critical to the requirements

Using Problem Frames and projections to analyze requirements for distributed systems

Subproblems in a problem frames decomposition frequently make use of projections of the complete problem context. One specific use of projec-tions occurs when an eventual implementation will be distributed, in which case a subproblem must interact with (use) the machine in a projection that represents another subproblem. We refer to subproblems used in this way as services, and propose an extension to projections to represent services as a spe-cial connection domain between subproblems. The extension provides signifi-cant benefits: verification of the symmetry of the interfaces, exposure of the machine-to-machine interactions, and prevention of accidental introduction of shared state. The extension’s usefulness is validated using a case study

Initiating organizational memories using ontology-based network analysis as a bootstrapping tool

An important problem for many kinds of knowledge systems is their initial set-up. It is difficult to choose the right information to include in such systems, and the right information is also a prerequisite for maximizing the uptake and relevance. To tackle this problem, most developers adopt heavyweight solutions and rely on a faithful continuous interaction with users to create and improve content. In this paper, we explore the use of an automatic, lightweight ontology-based solution to the bootstrapping problem, in which domain-describing ontologies are analysed to uncover significant yet implicit relationships between instances. We illustrate the approach by using such an analysis to provide content automatically for the initial set-up of an organizational memory

A Context-Oriented Extension of F#

Context-Oriented programming languages provide us with primitive constructs to adapt program behaviour depending on the evolution of their operational environment, namely the context. In previous work we proposed ML_CoDa, a context-oriented language with two-components: a declarative constituent for programming the context and a functional one for computing. This paper describes the implementation of ML_CoDa as an extension of F#.Comment: In Proceedings FOCLASA 2015, arXiv:1512.0694