Basics of qualitative research: Grounded theory procedures and techniques

How have Japanese companies become world leaders in the automotive and electronics industries, among others? What is the secret of their success? Two leading Japanese business experts, Ikujiro Nonaka and Hirotaka Takeuchi, are the first to tie the success of Japanese companies to their ability to create new knowledge and use it to produce successful products and technologies. In The Knowledge-Creating Company, Nonaka and Takeuchi provide an inside look at how Japanese companies go about creating this new knowledge organizationally. The authors point out that there are two types of knowledge: explicit knowledge, contained in manuals and procedures, and tacit knowledge, learned only by experience, and communicated only indirectly, through metaphor and analogy. U.S. managers focus on explicit knowledge. The Japanese, on the other hand, focus on tacit knowledge. And this, the authors argue, is the key to their success--the Japanese have learned how to transform tacit into explicit knowledge. To explain how this is done--and illuminate Japanese business practices as they do so--the authors range from Greek philosophy to Zen Buddhism, from classical economists to modern management gurus, illustrating the theory of organizational knowledge creation with case studies drawn from such firms as Honda, Canon, Matsushita, NEC, Nissan, 3M, GE, and even the U.S. Marines. For instance, using Matsushita's development of the Home Bakery (the world's first fully automated bread-baking machine for home use), they show how tacit knowledge can be converted to explicit knowledge: when the designers couldn't perfect the dough kneading mechanism, a software programmer apprenticed herself withthe master baker at Osaka International Hotel, gained a tacit understanding of kneading, and then conveyed this information to the engineers. In addition, the authors show that, to create knowledge, the best management style is neither top-down nor bottom-up, but rather what they call "middle-up-down," in which the middle managers form a bridge between the ideals of top management and the chaotic realities of the frontline. As we make the turn into the 21st century, a new society is emerging. Peter Drucker calls it the "knowledge society," one that is drastically different from the "industrial society," and one in which acquiring and applying knowledge will become key competitive factors. Nonaka and Takeuchi go a step further, arguing that creating knowledge will become the key to sustaining a competitive advantage in the future. Because the competitive environment and customer preferences changes constantly, knowledge perishes quickly. With The Knowledge-Creating Company, managers have at their fingertips years of insight from Japanese firms that reveal how to create knowledge continuously, and how to exploit it to make successful new products, services, and systems.

The knowledge-creating company

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

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Online communities – Designing Usability, Supporting Sociability

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

Different organizations or organizational units are likely to store and maintain different types of information about their software architectures. This inhibits effective management of architectural knowledge. We experimented with a model of architectural knowledge to characterize the use of architectural knowledge in four different organizations. Based on this experimentation we identified four perspectives on architectural knowledge management, and additionally adjusted the model to better align theory with practice. The refined model defines a minimal set of concepts with supposedly complete coverage of the architectural knowledge domain. Because of the minimalistic aspect of the model, we refer to it as a 'core model' of architectural knowledge. Supporting evidence for the validity of our model, i.e. the supposed complete coverage, has been obtained by an attempt to falsify this claim through a comparison with selected literature. Application of the core model to characterize the use of architectural knowledge indicates possible areas of improvement for architectural knowledge management in the four organizations.

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Architectural knowledge: getting to the core

Global software development (GSD) faces additional challenges as compared to single-site software development. Some of the better known challenges include temporal, geographical, and socio-cultural differences. To overcome these challenges, organizations need to revert to measures in order to deliver software in time in a distributed setting. Some of these measures may exist in the form of architectural rules: principles and statements on the software architecture that must be complied with throughout the organization. From the GSD literature we distilled four main GSD challenges and seven sub-challenges, or issues. For each issue, we list possible solutions and observe that solutions to GSD challenges may be obtained by adhering to architectural rules. We present a study on how two organizations involved in GSD solve the GSD challenges and issues. One of the organizations mainly uses rules regulating the architecture of the product. The other organization does not emphasize these architectural rules but rather focuses on the joint team effort in establishing and committing to measures that mainly pertain to the architecture process. We conclude that rules regulating a combination of both proves valuable in handling GSD challenges.

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Global Software Development: Are Architectural Rules the Answer?

Software architecture and software architecture practices become increasingly important for information systems since they enable reasoning on the design of the system. The concept of architectural knowledge, i.e. architectural design decisions and the resulting design, plays a pivotal role in architecture. In order to get the most out of architectural knowledge, we need insight into the ways in which architectural knowledge is used. Currently, we lack this insight. We performed survey-based research in the Netherlands to collect feedback on the importance of architectural knowledge for the daily work of practitioners in architecture. We present our findings using two perspectives: the architectural roles practitioners fulfill and the architecture level practitioners are engaged in. We use these perspectives to construct and reflect on the architect's mindset on architectural knowledge. This mindset of architects reveals an approach which is focused on 'to create and communicate' rather than 'to review and maintain' an architecture.

The architect's mindset

With the expansion of national markets beyond geographical limits, success of any business often depends on using software for competitive advantage. Furthermore, as technological boundaries are expanding, projects distributed across different geographical locations have become a norm for the software solution providers. Nevertheless, when implementing Global Software Development (GSD), organizations continue to face challenges in adhering to the development life cycle. The advent of the internet has supported GSD by bringing new concepts and opportunities resulting in benefits such as scalability, flexibility, independence, reduced cost, resource pools, and usage tracking. It has also caused the emergence of new challenges in the way software is being delivered to stakeholders. Application software and data on the cloud is accessed through services which follow SOA (Service Oriented Architecture) principles. In this paper, we present the challenges encountered in globally dispersed software projects. Based on goals mutually shared between GSD and the cloud computing paradigm, we propose to exploit cloud computing characteristics and privileges both as a product and as a process to improve GSD.

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Using the Cloud to Facilitate Global Software Development Challenges

In the past few years, an increasing interest in architectural knowledge is recognized in the software architecture community. Architectural knowledge is generally regarded as important to guide the development of software systems. With the trend of Global Software Development (GSD), the management of architectural knowledge becomes even more important due to the geographical, temporal, and socio-cultural distance innate to GSD. In this paper we build on the requirements engineering discipline to identify practices that can aid in overcoming GSD challenges and assess their applicability for management of architectural knowledge in a GSD setting. We provide a light-weight pattern language that we use to describe architectural knowledge management practices and provide a first validation of these practices from an ongoing case study.

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Viktor Clerc

Papers

Architectural knowledge: getting to the core

Global Software Development: Are Architectural Rules the Answer?

The architect's mindset

Using the Cloud to Facilitate Global Software Development Challenges

Towards architectural knowledge management practices for global software development