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

Manufacturing industry profoundly impact economic and societal progress. As being a commonly accepted term for research centers and universities, the Industry 4.0 initiative has received a splendid attention of the business and research community. Although the idea is not new and was on the agenda of academic research in many years with different perceptions, the term “Industry 4.0” is just launched and well accepted to some extend not only in academic life but also in the industrial society as well. While academic research focuses on understanding and defining the concept and trying to develop related systems, business models and respective methodologies, industry, on the other hand, focuses its attention on the change of industrial machine suits and intelligent products as well as potential customers on this progress. It is therefore important for the companies to primarily understand the features and content of the Industry 4.0 for potential transformation from machine dominant manufacturing to digital manufacturing. In order to achieve a successful transformation, they should clearly review their positions and respective potentials against basic requirements set forward for Industry 4.0 standard. This will allow them to generate a well-defined road map. There has been several approaches and discussions going on along this line, a several road maps are already proposed. Some of those are reviewed in this paper. However, the literature clearly indicates the lack of respective assessment methodologies. Since the implementation and applications of related theorems and definitions outlined for the 4th industrial revolution is not mature enough for most of the reel life implementations, a systematic approach for making respective assessments and evaluations seems to be urgently required for those who are intending to speed this transformation up. It is now main responsibility of the research community to developed technological infrastructure with physical systems, management models, business models as well as some well-defined Industry 4.0 scenarios in order to make the life for the practitioners easy. It is estimated by the experts that the Industry 4.0 and related progress along this line will have an enormous effect on social life. As outlined in the introduction, some social transformation is also expected. It is assumed that the robots will be more dominant in manufacturing, implanted technologies, cooperating and coordinating machines, self-decision-making systems, autonom problem solvers, learning machines, 3D printing etc. will dominate the production process. Wearable internet, big data analysis, sensor based life, smart city implementations or similar applications will be the main concern of the community. This social transformation will naturally trigger the manufacturing society to improve their manufacturing suits to cope with the customer requirements and sustain competitive advantage. A summary of the potential progress along this line is reviewed in introduction of the paper. It is so obvious that the future manufacturing systems will have a different vision composed of products, intelligence, communications and information network. This will bring about new business models to be dominant in industrial life. Another important issue to take into account is that the time span of this so-called revolution will be so short triggering a continues transformation process to yield some new industrial areas to emerge. This clearly puts a big pressure on manufacturers to learn, understand, design and implement the transformation process. Since the main motivation for finding the best way to follow this transformation, a comprehensive literature review will generate a remarkable support. This paper presents such a review for highlighting the progress and aims to help improve the awareness on the best experiences. It is intended to provide a clear idea for those wishing to generate a road map for digitizing the respective manufacturing suits. By presenting this review it is also intended to provide a hands-on library of Industry 4.0 to both academics as well as industrial practitioners. The top 100 headings, abstracts and key words (i.e. a total of 619 publications of any kind) for each search term were independently analyzed in order to ensure the reliability of the review process. Note that, this exhaustive literature review provides a concrete definition of Industry 4.0 and defines its six design principles such as interoperability, virtualization, local, real-time talent, service orientation and modularity. It seems that these principles have taken the attention of the scientists to carry out more variety of research on the subject and to develop implementable and appropriate scenarios. A comprehensive taxonomy of Industry 4.0 can also be developed through analyzing the results of this review.

Literature review of Industry 4.0 and related technologies

Software-product-line engineering has gained considerable momentum in recent years, both in industry and in academia. A software product line is a family of software products that share a common set of features. Software product lines challenge traditional analysis techniques, such as type checking, model checking, and theorem proving, in their quest of ensuring correctness and reliability of software. Simply creating and analyzing all products of a product line is usually not feasible, due to the potentially exponential number of valid feature combinations. Recently, researchers began to develop analysis techniques that take the distinguishing properties of software product lines into account, for example, by checking feature-related code in isolation or by exploiting variability information during analysis. The emerging field of product-line analyses is both broad and diverse, so it is difficult for researchers and practitioners to understand their similarities and differences. We propose a classification of product-line analyses to enable systematic research and application. Based on our insights with classifying and comparing a corpus of 123 research articles, we develop a research agenda to guide future research on product-line analyses.

https://www.infosun.fim.uni-passau.de/publications/docs/TAK14.pdf

A Classification and Survey of Analysis Strategies for Software Product Lines

We consider the OMG’s queries, views and transformations standard as applied to the specification of bidirectional transformations between models. We discuss what is meant by bidirectional transformations, and the model-driven development scenarios in which they are needed. We analyse the fundamental requirements on tools which support such transformations, and discuss some semantic issues which arise. In particular, we show that any transformation language sufficient to the needs of model-driven development would have to be able to express non-bijective transformations. We argue that a considerable amount of basic research is needed before suitable tools will be fully realisable, and suggest directions for this future research.

/pdf/bidirectional-model-transformations-in-qvt-semantic-issues-3x65k1xh7t.pdf

Bidirectional model transformations in QVT: semantic issues and open questions

The QVT Relations (QVT-R) transformation language allows the definition of bidirectional model transformations, which are required in cases where a two (or more) models must be kept consistent in the face of changes to either. A QVT-R transformation can be used either in checkonly mode, to determine whether a target model is consistent with a given source model, or in enforce mode, to change the target model. Although the most obvious semantic issues in the QVT standard concern the restoration of consistency, in fact even checkonly mode is not completely straightforward; this mode is the focus of this paper. We need to consider the overall structure of the transformation as given by when and where clauses, and the role of trace classes. In the standard, the semantics of QVT-R are given both directly, and by means of a translation to QVT Core, a language which is intended to be simpler. In this paper, we argue that there are irreconcilable differences between the intended semantics of QVT-R and those of QVT Core, so that the translation cannot be helpful. Treating QVT-R directly, we propose a simple game-theoretic semantics. We demonstrate that consistent models may not possess a single trace model whose objects can be read as traceability links in either direction. We briefly discuss the effect of variations in the rules of the game, to elucidate some design choices available to the designers of the QVT-R language.

/pdf/a-simple-game-theoretic-approach-to-checkonly-qvt-relations-7oqkrosaxs.pdf

A Simple Game-Theoretic Approach to Checkonly QVT Relations

The Model Driven Architecture (MDA) is an approach to develop software based on different models. There are separate models for the business logic and for platform specific details. Moreover, code can be generated automatically from these models. This makes transforma- tions a core technology for MDA and for model-based software engineering approaches in general. Query/View/Transformation (QVT) is the transformation technology recently proposed for this purpose by the OMG. Triple Graph Grammars (TGGs) are another transformation technology proposed in the mid-nineties, used for example in the FUJABA CASE tool. In contrast to many other transformation technologies, both QVT and TGGs declaratively define the relation between two models. With this definition, a transformation engine can execute a transformation in either direction and, based on the same definition, can also propagate changes from one model to the other. In this paper, we compare the concepts of the declarative languages of QVT and TGGs. It turns out that TGGs and declarative QVT have many concepts in common. In fact, QVT-Core can be mapped to TGGs. We show that QVT-Core can be implemented by transforming QVT-Core mappings to TGG rules, which can then be executed by a TGG transformation engine that performs the actual QVT transformation. Furthermore, we discuss an approach for mapping QVT-Relations to TGGs. Based on the semantics of TGGs, we clarify semantic gaps that we identified in the declarative languages of QVT and, furthermore, we show how TGGs can benefit from the concepts of QVT.

Comparing relational model transformation technologies: implementing Query/View/Transformation with Triple Graph Grammars

The Model Driven Architecture (MDA) is an approach to develop software based on different models. There are separate models for the business logic and for platform specific details. Moreover, code can be generated automatically from these models. This makes transformations a core technology for MDA. QVT (Query/View/Transformation) is the transformation technology recently proposed for this purpose by the OMG.TGGs (Triple Graph Grammars) are another transformation technology proposed in the mid-nineties, used for example in the FUJABA CASE tool. In contrast to many other transformation technologies, both QVT and TGGs declaratively define the relation between two models. With this relation definition, a transformation engine can execute a transformation in both directions and, based on the same definition, can also propagate changes from one model to the other.In this paper, we compare the concepts of QVT and TGGs. It turns out that TGGs and QVT have many concepts in common. In fact, fundamental parts of QVT-Core can be implemented by a TGG transformation engine. Moreover, we discuss how both technologies could profit from each other.

Reconciling TGGs with QVT

Model transformation plays a central role in Model-Driven Engineer- ing (MDE) and supporting bidirectionality is a current challenge with important applications. Triple Graph Grammars (TGGs) are a formally founded, bidirectional model transformation language shown by numerous case studies to be promising and useful in practice. TGGs have been researched for more than 15 years and multiple TGG tools are under active development. Although a common theoreti- cal foundation is shared, TGG tools differ considerably concerning expressiveness, applicability, efficiency, and the underlying translation algorithm. There currently exists neither a quantitative nor a qualitative overview and comparison of TGG tools and it is quite difficult to understand the different foci and corresponding strengths and weaknesses. Our contribution in this paper is to develop a set of criteria for com- paring TGG tools and to provide a concrete quantitative and qualitative comparison of three TGG tools.

/pdf/a-survey-of-triple-graph-grammar-tools-24js5t63ll.pdf

A Survey of Triple Graph Grammar Tools

The development of mechatronic systems demands the close collaboration of engineers from different domains. In the course of the development, this leads to the creation of a number of separate, but interdependent models which capture the domain-specific system aspects. Without harmonizing the domain-specific development processes, inconsistencies between the domain-specific models are likely to occur. If these inconsistencies remain undetected, the system integration will fail, which is leading to increased development time and costs. As a first step to prevent these problems, we propose a cross-domain system specification in the early conceptual design phase. Furthermore, as the novel contribution of this paper, we show how model transformation techniques can be employed to, firstly, derive initial domain-specific models for the subsequent domain-specific development and, secondly, to propagate domain-spanning relevant changes that may occur between those models. We show how the domain-spanning relevance of changes may be detected automatically and we discuss where expert decisions are indispensable. We implemented the approach in our development environment.

/pdf/management-of-cross-domain-model-consistency-during-the-1hyltstwsi.pdf

Management of Cross-Domain Model Consistency during the Development of Advanced Mechatronic Systems

Graph transformation provides an intuitive mechanism for capturing model transformations. In the current paper, we investigate and compare various graph transformation tools using a compact practical model transformation case study carried out as part of the AGTIVE 2007 Tool Contest [22]. The aim of this case study is to generate formal CSP processes from high-level UML activity diagrams, which enables to carry out mathematical analysis of the system under design.

/pdf/transformation-of-uml-models-to-csp-a-case-study-for-graph-1cq0tn98gj.pdf

Joel Greenyer

Papers

Comparing relational model transformation technologies: implementing Query/View/Transformation with Triple Graph Grammars

Reconciling TGGs with QVT

A Survey of Triple Graph Grammar Tools

Management of Cross-Domain Model Consistency during the Development of Advanced Mechatronic Systems

Transformation of UML Models to CSP: A Case Study for Graph Transformation Tools