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

Eric Ries was born in September 1978. He graduated from Yale University and moved to silicon Valley in the beginning of the millennium. He pioneered the philosophy of Lean Startup, based on his experience with multiple startups, primary being IMVU which he co-founded along with Will Harvey in 2004. Eric Ries originated his Lean Startup philosophy after getting inspired from the Japanese Philosophy of Lean Manufacturing.

The Lean Startup:How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses

Continuous software engineering: A roadmap and agenda

The Lean Startup: How today's entrepreneurs use continuous innovation to create radically successful businesses

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

A swift execution from idea to market has become a key competitive advantage for software companies to enable them to survive and grow in turbulent business environments. To combat this challenge, companies are using hackathons. A hackathon is a highly engaging, continuous event in which people in small groups produce working software prototypes in a limited amount of time. F-Secure, a software product company, views hackathons as a possible solution to the fundamental business problem of how to make revenue from an idea, spanning the phases from creating the idea to producing a software prototype. However, hackathons pose the challenge of how to transform those promising prototypes into finalized products that create revenue and real business value.

/pdf/what-are-hackathons-for-59mhyh3cx7.pdf

What are Hackathons for

Context The analysis and selection of requirements are important parts of any release planning process. Previous studies on release planning have focused on plan-driven optimization models. Unfortunately, solving the release planning problem mechanistically is difficult in an agile development context. Objective We describe how a release planning method was employed in two case projects in F-Secure, a large Finnish software company. We identify the benefits which the projects gained from the method, and analyze challenges in the cases and improvements made to the method during the case projects. Method We observed five release planning events and four retrospectives and we conducted surveys in the first two events. We conducted six post-project interviews. We conjoined the observation notes, survey results and interviews and analyzed them qualitatively and quantitatively. Results The focal point of the method was release planning events where the whole project organization gathered to plan the next release. The planning was conducted by the development teams in close collaboration with each other and with the other stakeholders. We identified ten benefits which included improved communication, transparency, dependency management and decision making. We identified nine challenges which included the lacking preparation and prioritization of requirements, unrealistic schedules, insufficient architectural planning and lacking agile mindset. The biggest improvements to the method were the introduction of frequent status checks and a big visible planning status board. Conclusion The release planning method ameliorated many difficult characteristics of the release planning problem but its efficiency was negatively affected by the performing organization that was in transition from a plan-driven to an agile development mindset. Even in this case the benefits clearly outweighed the challenges and the method enabled the early identification of the issues in the project.

/pdf/operational-release-planning-in-large-scale-scrum-with-4uxfih6qzp.pdf

Operational release planning in large-scale Scrum with multiple stakeholders – A longitudinal case study at F-Secure Corporation

The rapid downfall of the Nokia software ecosystem has radically altered the landscape of software industry in Finland in recent years. There has been a shift from largely corporate driven way of working, which is often dominant in large companies, to more agile practices, and in general software organizations are seeking new, leaner ways of composing, delivering, and using software also inside already established companies. To accelerate this transformation in large scale, a collaborative research program has been created, called Need for Speed (N4S). In this paper, we give an insight to the joint goals and concrete actions of the program and discuss the motivations of individual companies that are participating in the program. As one concrete goal of the project, we introduce the concept of Mercury business, where the principles of the Lean startup framework are applied in a more conventional industrial setting.

/pdf/from-agile-software-development-to-mercury-business-2qhpag5agf.pdf

From Agile Software Development to Mercury Business

A hackathon (hacking marathon) is an event to innovate and develop prototypes, typically lasting at most a few days. Despite several innovations having been reported resulting from hackathons and the increasing popularity of hackathons, results about, organizing of, and experiences regarding hackathons have been scarcely reported. We studied a hackathon as a means to assess a device-centric cloud ecosystem in industrial settings. We provide a descriptive account of a three-days hackathon. The experience was that the hackathon was realistic as well as an efficient and effective assessment of the requirements and design of the ecosystem, providing guidance for future development. We also summarize the lessons learned about successfully organizing a hackathon. The results also highlight encouraging experience about the hackathon among the participants in terms of the social benefits, such as collaboration, inspiration, and work motivation, resulting in repeating hackathons for various purposes in the near future. In general, the results indicate a hackathon as a promising new approach in software engineering, where speed of development is becoming essential.

Industrial Experiences of Organizing a Hackathon to Assess a Device-centric Cloud Ecosystem

Context: Traditional technology transfer models rely on the assumption that innovations are created in academia, after which they are transferred to industry using a sequential flow of activities. This model is outdated in contemporary software engineering research that is done in close collaboration between academia and industry and in large consortia rather than on a one-on-one basis. In the new setup, research can be viewed as continuous co-experimentation, where industry and academia closely collaborate and iteratively and jointly discover problems and develop, test, and improve solutions. Objective: The objective of the paper is to answer the following research questions: How can high-quality, ambitious software engineering research in a collaborative setup be conducted quickly and on a large scale? How can real-time business feedback to continuously improve candidate solutions be gained? Method: The proposed model has been created, refined, and evaluated in two large, national Finnish software research programs. For this paper, we conducted thematic interviews with representatives of four companies who participated in these programs. Results: The fundamental change is in the mindset of the participants from technology push by academia to technology pull by companies, resulting in co-creation. Furthermore, continuous cooperation between participants enables solutions to evolve in rapid cycles and forms a scalable model of interaction between research institutes and companies. Conclusions: The multifaceted nature of software engineering research calls for numerous approaches. In particular, when working with human-related topics such as company culture and development methods, many discoveries result from seamless collaboration between companies and research institutes.

/pdf/continuous-and-collaborative-technology-transfer-software-19jmu14w1o.pdf

Janne Järvinen

Papers

What are Hackathons for

Operational release planning in large-scale Scrum with multiple stakeholders – A longitudinal case study at F-Secure Corporation

From Agile Software Development to Mercury Business

Industrial Experiences of Organizing a Hackathon to Assess a Device-centric Cloud Ecosystem

Continuous and collaborative technology transfer: Software engineering research with real-time industry impact