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

An orchestrated survey of methodologies for automated software test case generation

The B Book

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

As the technology associated to the ?Web Services? trend gains significant adoption, the need for a corresponding design approach becomes increasingly important. This paper introduces a foundational model for designing (composite) services. The innovation of this model lies in the identification of four interrelated viewpoints (interface behaviour, provider behaviour, choreography, and orchestration) and their formalization from a control-flow perspective in terms of Petri nets. By formally capturing the interrelationships between these viewpoints, the proposal enables the static verification of the consistency of composite services designed in a cooperative and incremental manner. A proof-of-concept simulation and verification tool has been developed to test the possibilities of the proposed model.

Service-oriented Design : A Multi-viewpoint Approach

We present a computational framework for automatic synthesis of control and communication strategies for a robotic team from task specifications that are given as regular expressions about servicing requests in an environment. We assume that the location of the requests in the environment and the robot capacities and cooperation requirements to service the requests are known. Our approach is based on two main ideas. First, we extend recent results from formal synthesis of distributed systems to check for the distributability of the task specification and to generate local specifications, while accounting for the service and communication capabilities of the robots. Second, by using a technique that is inspired by linear temporal logic model checking, we generate individual control and communication strategies. We illustrate the method with experimental results in our robotic urban-like environment.

Formal Approach to the Deployment of Distributed Robotic Teams

Model-Driven Engineering (MDE) has been promoted for many years as a means for handling the complexity of software development by raising the abstraction level and automating labor-intensive and error-prone tasks. However, there is little empirical evidence of the acceptance of MDE in industry which is the subject of this paper. The goal of this empirical study was to investigate the state of the practice of applying MDE and factors considered as important for its adoption. The subjects were developers of four large companies participating in a research project. The collected data came from multiple sources and covered the results of tool evaluations, interviews, and a survey. Among the factors, we found perceived usefulness, ease of use and the maturity of the tools to be important determinants for the adoption of MDE. We also discuss challenges with adopting MDE and present suggestions on how to succeed with the adoption process.

An empirical study of the state of the practice and acceptance of model-driven engineering in four industrial cases

There have been few experience reports from industry on how Model-Driven Engineering (MDE) is applied and what the benefits are. This paper summarizes the experiences of three large industrial participants in a European research project with the objective of developing techniques and tools for applying MDE on the development of large and complex software systems. The participants had varying degrees of previous experience with MDE. They found MDE to be particularly useful for providing abstractions of complex systems at multiple levels or from different viewpoints, for the development of domain-specific models that facilitate communication with non-technical experts, for the purposes of simulation and testing, and for the consumption of models for analysis, such as performance-related decision support and system design improvements. From the industrial perspective, a methodology is considered to be useful and cost-efficient if it is possible to reuse solutions in multiple projects or products. However, developing reusable solutions required extra effort and sometimes had a negative impact on the performance of tools. While the companies identified several benefits of MDE, merging different tools with one another in a seamless development environment required several transformations, which increased the required implementation effort and complexity. Additionally, user-friendliness of tools and the provision of features for managing models of complex systems were identified as crucial for a wider industrial adoption of MDE.

Where does model-driven engineering help? Experiences from three industrial cases

We apply the theory of asynchronous automata to the synthesis problem of closed distributed systems. We use safe asynchronous automata as implementation model, and characterise the languages they accept. We analyze the complexity of the synthesis problem in our framework. Theorems by Zielonka and Morin are then used to develop and implement a synthesis algorithm. Finally, we apply the developed algorithms to the classic problem of mutual exclusion.

https://www.fmi.uni-stuttgart.de/szs/publications/stefanan/sem03concur.pdf

Synthesis of Distributed Algorithms Using Asynchronous Automata

This thesis investigates the synthesis problem for two classes of distributed transition systems: synchronous products and asynchronous automata. The underlying structure of these models consist of local automata synchronizing on common actions. The synthesis problem discussed is as follows: Given a global specification as a transition system TS and a distribution pattern D, find a distributed transition system over D whose global state space is equivalent' to TS. As criteria for the correctness of the (distributed) implementation vs. the specification (i.e., their equivalence') we use: transition system isomorphism, language equivalence, and bisimilarity respectively. In particular, the synthesis of asynchronous automata modulo language equivalence is a notoriously hard problem solved by Zielonka at the end of the 80s. One of the motivations behind our work was to bring this theory closer to practical applications.

From the theoretical point of view, we conduct a detailed analysis of the synthesis problem for both models of distributed systems, look at effective algorithmic approaches and draw a map of computational complexity results. E.g., we provide several matching lower and upper complexity bounds for the distributed implementability problem. 

From the practical perspective, we provide prototype implementations for most of the synthesis algorithms discussed in the thesis. Moreover, we offer assistance when a given specification is not distributable by trying to modify this specification such that distributed synthesis can be applied. By using several heuristics to overcome the classical state space explosion, we are able to automatically generate small distributed algorithms for problems such as mutual exclusion. 
Diese Dissertation erforscht das Syntheseproblem fur zwei verschiedene Arten von verteilten Transitionssysteme: synchrone Produkte und asynchrone Automaten. Diese Modelle bestehen aus lokalen Automaten, die sich uber gemeinsame Aktionen synchronisieren. Das betrachtete Syntheseproblem ist Folgendes: Fur ein gegebenes Transitionssystem TS und eine Verteilungsstruktur D sucht man ein verteiltes Transitionssystem uber D mit einem globalen Zustandraum aquivalent' zu TS. Als Kriterien fur die Korrektheit der Implementierung bezuglich der Spezifikation (d.h., ihre Aquivalenz'), verwenden wir Transitionssystem-Isomorphismus, Sprachenaquivalenz bzw. Bisimilaritat. Insbesondere ist die Synthese der asynchronen Automaten Modulo-Sprachenaquivalenz ein bekanntes hartes Problem, das von Zielonka Ende der 80iger Jahre gelost wurde. Einer der Beweggrunde hinter unserer Arbeit war diese Theorie naher an praktische Anwendungen zu holen.

Aus theoretische Sicht leiten wir eine ausfuhrliche Analyse des Syntheseproblems fur beide Modelle der verteilten Systeme, betrachten wirkungsvolle Algorithmische Verfahren und erarbeiten eine Ubersicht der Berechnungskomplexitat. So z.B. stellen wir einige untere und obere Komplexitatsgrenzen fur das verteilte Implementierungsproblem zur Verfugung. 
Von einer praktischen Ansicht leifern wir Prototypimplementierungen fur die meisten Synthesealgorithmen, die in diese Dissertation besprochen werden. Auserdem bieten wir Unterstutzung an, falls eine gegebene Spezifikation nicht verteilbar ist und versuchen, sie zu andern, so das verteilte Synthese moglich wird. Indem wir einige Heuristiken verwenden, zum der klassischen Zustandraumsexplosion zu uberwinden, sind wir in der Lage, kleine verteilte Algorithmen fur Probleme, wie z.B. der gegenseitige Ausschlus (Mutex), automatisch zu erzeugen.

Alin Stefanescu

Papers

Formal Approach to the Deployment of Distributed Robotic Teams

An empirical study of the state of the practice and acceptance of model-driven engineering in four industrial cases

Where does model-driven engineering help? Experiences from three industrial cases

Synthesis of Distributed Algorithms Using Asynchronous Automata

Automatic synthesis of distributed transition systems