Essentially a software system's utility is determined by both its functionality and its non-functional characteristics, such as usability, flexibility, performance, interoperability and security. Nonetheless, there has been a lop-sided emphasis in the functionality of the software, even though the functionality is not useful or usable without the necessary non-functional characteristics. In this chapter, we review the state of the art on the treatment of non-functional requirements (hereafter, NFRs), while providing some prospects for future directions.

/pdf/on-non-functional-requirements-in-software-engineering-3a6a23qlo6.pdf

On Non-Functional Requirements in Software Engineering

From the Book:
“Clean code that works” is Ron Jeffries’ pithy phrase. The goal is clean code that works, and for a whole bunch of reasons:
Clean code that works is a predictable way to develop. You know when you are finished, without having to worry about a long bug trail.Clean code that works gives you a chance to learn all the lessons that the code has to teach you. If you only ever slap together the first thing you think of, you never have time to think of a second, better, thing. Clean code that works improves the lives of users of our software.Clean code that works lets your teammates count on you, and you on them.Writing clean code that works feels good.But how do you get to clean code that works? Many forces drive you away from clean code, and even code that works. Without taking too much counsel of our fears, here’s what we do—drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short). 
In Test-Driven Development, you:
Write new code only if you first have a failing automated test.Eliminate duplication.

Two simple rules, but they generate complex individual and group behavior. Some of the technical implications are:You must design organically, with running code providing feedback between decisionsYou must write your own tests, since you can’t wait twenty times a day for someone else to write a testYour development environment must provide rapid response to small changesYour designs must consist of many highly cohesive, loosely coupled components, just to make testing easy

The two rules imply an order to the tasks ofprogramming:
1. Red—write a little test that doesn’t work, perhaps doesn’t even compile at first
2. Green—make the test work quickly, committing whatever sins necessary in the process
3. Refactor—eliminate all the duplication created in just getting the test to work


Red/green/refactor. The TDD’s mantra.

Assuming for the moment that such a style is possible, it might be possible to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved. If so, writing only code demanded by failing tests also has social implications:
If the defect density can be reduced enough, QA can shift from reactive to pro-active workIf the number of nasty surprises can be reduced enough, project managers can estimate accurately enough to involve real customers in daily developmentIf the topics of technical conversations can be made clear enough, programmers can work in minute-by-minute collaboration instead of daily or weekly collaborationAgain, if the defect density can be reduced enough, we can have shippable software with new functionality every day, leading to new business relationships with customers


So, the concept is simple, but what’s my motivation? Why would a programmer take on the additional work of writing automated tests? Why would a programmer work in tiny little steps when their mind is capable of great soaring swoops of design? Courage.
Courage
Test-driven development is a way of managing fear during programming. I don’t mean fear in a bad way, pow widdle prwogwammew needs a pacifiew, but fear in the legitimate, this-is-a-hard-problem-and-I-can’t-see-the-end-from-the-beginning sense. If pain is nature’s way of saying “Stop!”, fear is nature’s way of saying “Be careful.” Being careful is good, but fear has a host of other effects:
Makes you tentativeMakes you want to communicate lessMakes you shy from feedbackMakes you grumpy

None of these effects are helpful when programming, especially when programming something hard. So, how can you face a difficult situation and:
Instead of being tentative, begin learning concretely as quickly as possible.Instead of clamming up, communicate more clearly.Instead of avoiding feedback, search out helpful, concrete feedback.(You’ll have to work on grumpiness on your own.) 

Imagine programming as turning a crank to pull a bucket of water from a well. When the bucket is small, a free-spinning crank is fine. When the bucket is big and full of water, you’re going to get tired before the bucket is all the way up. You need a ratchet mechanism to enable you to rest between bouts of cranking. The heavier the bucket, the closer the teeth need to be on the ratchet.

The tests in test-driven development are the teeth of the ratchet. Once you get one test working, you know it is working, now and forever. You are one step closer to having everything working than you were when the test was broken. Now get the next one working, and the next, and the next. By analogy, the tougher the programming problem, the less ground should be covered by each test.

Readers of Extreme Programming Explained will notice a difference in tone between XP and TDD. TDD isn’t an absolute like Extreme Programming. XP says, “Here are things you must be able to do to be prepared to evolve further.” TDD is a little fuzzier. TDD is an awareness of the gap between decision and feedback during programming, and techniques to control that gap. “What if I do a paper design for a week, then test-drive the code? Is that TDD?” Sure, it’s TDD. You were aware of the gap between decision and feedback and you controlled the gap deliberately.

That said, most people who learn TDD find their programming practice changed for good. “Test Infected” is the phrase Erich Gamma coined to describe this shift. You might find yourself writing more tests earlier, and working in smaller steps than you ever dreamed would be sensible. On the other hand, some programmers learn TDD and go back to their earlier practices, reserving TDD for special occasions when ordinary programming isn’t making progress.

There are certainly programming tasks that can’t be driven solely by tests (or at least, not yet). Security software and concurrency, for example, are two topics where TDD is not sufficient to mechanically demonstrate that the goals of the software have been met. Security relies on essentially defect-free code, true, but also on human judgement about the methods used to secure the software. Subtle concurrency problems can’t be reliably duplicated by running the code.

Once you are finished reading this book, you should be ready to:
Start simplyWrite automated testsRefactor to add design decisions one at a time

This book is organized into three sections.
An example of writing typical model code using TDD. The example is one I got from Ward Cunningham years ago, and have used many times since, multi-currency arithmetic. In it you will learn to write tests before code and grow a design organically.An example of testing more complicated logic, including reflection and exceptions, by developing a framework for automated testing. This example also serves to introduce you to the xUnit architecture that is at the heart of many programmer-oriented testing tools. In the second example you will learn to work in even smaller steps than in the first example, including the kind of self-referential hooha beloved of computer scientists.Patterns for TDD. Included are patterns for the deciding what tests to write, how to write tests using xUnit, and a greatest hits selection of the design patterns and refactorings used in the examples.

I wrote the examples imagining a pair programming session. If you like looking at the map before wandering around, you may want to go straight to the patterns in Section 3 and use the examples as illustrations. If you prefer just wandering around and then looking at the map to see where you’ve been, try reading the examples through and refering to the patterns when you want more detail about a technique, then using the patterns as a reference.

Several reviewers have commented they got the most out of the examples when they started up a programming environment and entered the code and ran the tests as they read.

A note about the examples. Both examples, multi-currency calculation and a testing framework, appear simple. There are (and I have seen) complicated, ugly, messy ways of solving the same problems. I could have chosen one of those complicated, ugly, messy solutions to give the book an air of “reality.” However, my goal, and I hope your goal, is to write clean code that works. Before teeing off on the examples as being too simple, spend 15 seconds imagining a programming world in which all code was this clear and direct, where there were no complicated solutions, only apparently complicated problems begging for careful thought. TDD is a practice that can help you lead yourself to exactly that careful thought.

Test Driven Development: By Example

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

Thank you very much for reading cognition in the wild. Maybe you have knowledge that, people have look hundreds times for their favorite books like this cognition in the wild, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. cognition in the wild is available in our digital library an online access to it is set as public so you can download it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the cognition in the wild is universally compatible with any devices to read.

Cognition In The Wild

Information privacy refers to the desire of individuals to control or have some influence over data about themselves. Advances in information technology have raised concerns about information privacy and its impacts, and have motivated Information Systems researchers to explore information privacy issues, including technical solutions to address these concerns. In this paper, we inform researchers about the current state of information privacy research in IS through a critical analysis of the IS literature that considers information privacy as a key construct. The review of the literature reveals that information privacy is a multilevel concept, but rarely studied as such. We also find that information privacy research has been heavily reliant on studentbased and USA-centric samples, which results in findings of limited generalizability. Information privacy research focuses on explaining and predicting theoretical contributions, with few studies in journal articles focusing on design and action contributions. We recommend that future research should consider different levels of analysis as well as multilevel effects of information privacy. We illustrate this with a multilevel framework for information privacy concerns. We call for research on information privacy to use a broader diversity of sampling populations, and for more design and action information privacy research to be published in journal articles that can result in IT artifacts for protection or control of information privacy.

http://ftp.misq.org/skin/frontend/default/misq/pdf/appendices/2011/BelangerCrosslerAppendices.pdf

Privacy in the digital age: a review of information privacy research in information systems

In this article, we introduce the application of rigorous analysis procedures to goal models to provide several benefits beyond the initial act of modeling. Such analysis can allow modelers to assess the satisfaction of goals, facilitate evaluation of high-level design alternatives, help analysts decide on the high-level requirements and design of the system, test the sanity of a model, and support communication and learning. The analysis of goal models can be done in very different ways depending on the nature of the model and the purpose of the analysis. In our work, we use the Goal-oriented Requirement Language (GRL), which is part of the User Requirements Notation (URN). URN, a new Recommendation of the International Telecommunications Union, provides the first standard goal-oriented language. Using GRL, we develop an approach to analysis that can be done by evaluating qualitative or quantitative satisfaction levels of the actors and intentional elements (e.g., goals and tasks) composing the model. Initial satisfaction levels for some of the intentional elements are provided in a strategy and then propagated to the other intentional elements of the model through the various links that connect them. The results allow for an assessment of the relative effectiveness of design alternatives at the requirements level. Although no specific propagation algorithm is imposed in the URN standard, different criteria for defining evaluation mechanisms are described. We provide three algorithms (quantitative, qualitative, and hybrid) as examples, which satisfy the constraints imposed by the standard. These algorithms have been implemented in the open-source jUCMNav tool, an Eclipse-based editor for URN models. The algorithms are presented and compared with the help of a telecommunication system example. © 2010 Wiley Periodicals, Inc.

http://cserg.site.uottawa.ca/ucm/pub/UCM/VirLibIJIS09/IJIS2010-Web.pdf

Evaluating goal models within the goal-oriented requirement language

A Globally Optimal k-Anonymity Method for the De-Identification of Health Data

The User Requirements Notation (URN), standardized by the International Telecommunication Union in 2008, is used to model and analyze requirements with goals and scenarios. This paper describes the first ten years of development of URN, and discusses ongoing efforts targeting the next ten years. We did a study inspired by the systematic literature review approach, querying five major search engines and using the existing URN Virtual Library. Based on the 281 scientific publications related to URN we collected and analyzed, we observe a shift from a more conventional use of URN for telecommunications and reactive systems to business process management and aspect-oriented modeling, with relevant extensions to the language being proposed. URN also benefits from a global and active research community, although industrial contributions are still sparse. URN is now a leading language for goal-driven and scenario-oriented modeling with a promising future for many application domains.

User Requirements Notation: The First Ten Years, The Next Ten Years (Invited Paper)

Introduction to the user requirements notation: learning by example

BPMN 2.0 is an OMG standard and one of the leading process modelling notations. Although the current language specification recognises the importance of defining a visual notation carefully, it does so by relying on common sense, intuition and emulation of common practices, rather than by adopting a rigorous scientific approach. This results in a number of suboptimal language design decisions that may impede effective model-mediated communication between stakeholders. We demonstrate and illustrate this by looking at BPMN 2.0 through the lens of the Physics of Notations, a collection of evidence-based principles that together form a theory of notation design. This work can be considered a first step towards making BPMN 2.0's visual notation more cognitively effective.

Daniel Amyot

Papers

Evaluating goal models within the goal-oriented requirement language

A Globally Optimal k-Anonymity Method for the De-Identification of Health Data

User Requirements Notation: The First Ten Years, The Next Ten Years (Invited Paper)

Introduction to the user requirements notation: learning by example

Analysing the cognitive effectiveness of the BPMN 2.0 visual notation