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

A prevalent and pervasive view of designing is that it can be modeled using variables and decisions made about what values should be taken by these variables. The activity of designing is carried out with the expectation that the designed artifact will operate in the natural world and the social world. These worlds impose constraints on the variables and their values; so, design could be described as a goal-oriented, constrained, decision- making activity. However, design distinguish- es itself from other similarly described activities not only by its domain but also by additional necessary features. Designing involves exploration, exploring what variables might be appropriate. The process of explo- ration involves both goal variables and deci- sion variables. In addition, designing involves learning: Part of the exploration activity is learning about emerging features as a design proceeds. Finally, design activity occurs within two contexts: the context within which the designer operates and the context produced by the developing design itself. The designer’s perception of what the context is affects the implication of the context on the design. The context shifts as the designer’s perceptions change. Design activity can be now characterized as a goal-oriented, con- strained, decision-making, exploration, and learning activity that operates within a con- text that depends on the designer’s percep- tion of the context.

Design Prototypes: A Knowledge Representation Schema for Design

This book contains the refereed proceedings of the 11th International Conference on Agile Software Development, XP 2010, held in Trondheim, Norway, in June 2010. In order to better evaluate the submitted papers and to highlight the applicational aspects of agile software practices, there were two different program committees, one for research papers and one for experience reports. Regarding the research papers, 11 out of 39 submissions were accepted as full papers; and as far as the experience reports were concerned, the respective number was 15 out of 50 submissions. In addition to these papers, this volume also includes the short research papers, the abstracts of the posters, the position papers of the PhD symposium, and the abstracts of the panel on Collaboration in an Agile World.

Agile Processes in Software Engineering and Extreme Programming: 11th International Conference, XP 2010, Trondheim, Norway, June 1-4, 2010, Proceedings ... Notes in Business Information Processing)

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Designing Software Product Lines With Uml From Use Cases To Pattern Based Software Architectures

Software engineering in the automotive domain faces outstanding challenges in terms of quality, cost, and functional complexity. To ensure process and product excellence, Bosch Gasoline Systems (GS) introduced a process improvement program based on Capability Maturity Model Integration (CMMI) and adopted the product line approach (PLA). Business strategies for software products, software sharing with customers, and common solutions for diesel and gasoline engine control software are inputs for the product line architecture. The steps towards the PLA started with an evaluation project followed by an evolutionary rollout. The lack of suitable mechanisms and tools for some crucial nonfunctional requirements is a major drawback for introducing the PLA. Nevertheless, GS considers it the only systematic approach to dealing with current and future challenges.

Introducing PLA at Bosch Gasoline systems: Experiences and practices

Model-Based Requirement Engineering to Support Development of Complex Systems

Since the major revision 2 of the Unified Modeling Language (UML), activity diagrams have acquired many new features, e.g. hierarchy, data flow and signals. Thus, UML 2 activity diagrams are one of the most versatile formalisms, and can be applied in different domains. Activity diagrams are supported by a number of tools enabling for instance the execution of activity models. Based on the domain these tools have specific requirements and need an adequate interpretation of the informal UML semantics. Wepropose a foundation for a frameworkwhich enables composition of operational semantics out of fundamental semantic constructs. These constructs provide options for domain specific variants. As an example, we introduce two different tool developments based on particular operational semantics composed by our approach. One tool focuses on the modeling of information systems whereas the other tool is aimed at the modeling of reactive systems.

Defining Domain Specific Operational Semantics for Activity Diagrams

The control of degraded products, waste streams and secondary raw materials that can be produced from those must be in line with demand within the framework of an Advanced Circular Economy. Material requirements are developing dynamically depending on product development and consumer behavior. Accordingly, the recycling system must also behave dynamically and predictively and has to be transformed into stable, efficient but flexible process routes. This can also lead to a shift in the significance and sequence of the respective materials of main and secondary value in a process chain. This paper presents a novel approach for a smart and predictive circular economy. The approach consists of three major parts: An open information marketplace to meet information needs, suitable economic assessment and planning methods, and a dynamic optimization of the recycling process chain, e.g., selection of process steps and their sequence.

Predictive and Flexible Circular Economy Approaches for Highly Integrated Products and their Materials as Given in E-Mobility and ICT

Vehicle functions for engine control units are modeled using a set of software units, so-called modules, specifying the discrete and continuous behavior of the corresponding function As required by ISO26262, each module needs to be tested separately Established techniques for model-based testing necessitate a requirements specification from which a test model can be derived In practice, requirements are specified by natural language and on the level of whole vehicle functions instead of modules so that test models on module level can not be derived directly Therefore, we propose a systematic model-based, test-driven approach to design a specification on the level of modules, which is directly testable We demonstrate our approach on a Selective Catalytic Reduction system, a real world case study from automotive software engineering

A test-driven approach for model-based development of powertrain functions

Most automobile manufacturers maintain many vehicle types to keep a successful position on the market. Through the further development all vehicle types gain a diverse amount of new functionality. Additional features have to be supported by the car’s software. For time efficient accomplishment, usually the existing electronic control unit (ECU) code is extended.

Christoph Knieke

Papers

Model-Based Requirement Engineering to Support Development of Complex Systems

Defining Domain Specific Operational Semantics for Activity Diagrams

Predictive and Flexible Circular Economy Approaches for Highly Integrated Products and their Materials as Given in E-Mobility and ICT

A test-driven approach for model-based development of powertrain functions

Mastering Erosion of Software Architecture in Automotive Software Product Lines