There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

物件導向軟體之架構(Object-Oriented Software Construction)探討

http://emamispnu.persiangig.com/Projects/Cloud%20Computing/science_2.pdf

Review: A survey on security issues in service delivery models of cloud computing

Much has been written about theory and practice in the law, and the tension between practitioners and theorists. Judges do not cite theoretical articles often; they rarely "apply" theories to particular cases. These arguments are not revisited. Instead the Essay explores the working and interaction of theory and practice, practitioners and theorists. The Essay starts with a story about solving a legal issue using our intellectual tools - theory, practice, and their progenies: experience and "gut." Next the Essay elaborates on the nature of theory, practice, experience and "gut." The third part of the Essay discusses theories that are helpful to practitioners and those that are less helpful. The Essay concludes that practitioners theorize, and theorists practice. They use these intellectual tools differently because the goals and orientations of theorists and practitioners, and the constraints under which they act, differ. Theory, practice, experience and "gut" help us think, remember, decide and create. They complement each other like the two sides of the same coin: distinct but inseparable.

Of Theory and Practice

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

Recent technology trends in the Web Services (WS) domain indicate that a solution eliminating the presumed complexity of the WS-* standards may be in sight: advocates of REpresentational State Transfer (REST) have come to believe that their ideas explaining why the World Wide Web works are just as applicable to solve enterprise application integration problems and to simplify the plumbing required to build service-oriented architectures. In this paper we objectify the WS-* vs. REST debate by giving a quantitative technical comparison based on architectural principles and decisions. We show that the two approaches differ in the number of architectural decisions that must be made and in the number of available alternatives. This discrepancy between freedom-from-choice and freedom-of-choice explains the complexity difference perceived. However, we also show that there are significant differences in the consequences of certain decisions in terms of resulting development and maintenance costs. Our comparison helps technical decision makers to assess the two integration styles and technologies more objectively and select the one that best fits their needs: REST is well suited for basic, ad hoc integration scenarios, WS-* is more flexible and addresses advanced quality of service requirements commonly occurring in enterprise computing.

/pdf/restful-web-services-vs-big-web-services-making-the-right-2tg7890x7z.pdf

Restful web services vs. "big"' web services: making the right architectural decision

Some microservices proponents claim that microservices form a new architectural style; in contrast, advocates of service-oriented architecture (SOA) argue that microservices merely are an implementation approach to SOA. This overview and vision paper first reviews popular introductions to microservices to identify microservices tenets. It then compares two microservices definitions and contrasts them with SOA principles and patterns. This analysis confirms that microservices indeed can be seen as a development- and deployment-level variant of SOA; such microservices implementations have the potential to overcome the deficiencies of earlier approaches to SOA realizations by employing modern software engineering paradigms and Web technologies such as domain-driven design, RESTful HTTP, IDEAL cloud application architectures, polyglot persistence, lightweight containers, a continuous DevOps approach to service delivery, and comprehensive but lean fault management. However, these paradigms and technologies also cause a number of additional design choices to be made and create new options for many "distribution classics" type of architectural decisions. As a result, the cognitive load for (micro-)services architects increases, as well as the design, testing and maintenance efforts that are required to benefit from an adoption of microservices. To initiate and frame the buildup of architectural knowledge supporting microservices projects, this paper compiles related practitioner questions; it also derives research topics from these questions. The paper concludes with a summarizing position statement: microservices constitute one particular implementation approach to SOA (service development and deployment).

Microservices tenets

https://soadecisions.org/download/SOAD-JSS82CameraReadyFinal.pdf

Managing architectural decision models with dependency relations, integrity constraints, and production rules

Effective and affordable business-to-business process integration is a key success factor in the telecommunications industry. A large telecommunication wholesaler, supplying its services to more than 150 different service retailers, enhanced the process integration capabilities of its core order management system through widespread use of SOA, business process choreography and Web services concepts. This core order management system processes 120 different complex order types.On this project, challenging requirements such as complexity of business process models and multi-channel accessibility turned out to be true proof points for the applied SOA concepts, tools, and runtime environments. To implement an automated and secured business-to-business Web services channel and to introduce a process choreography layer into a large existing application were two of the key requirements that had to be addressed. The solution complies with the Web Services Interoperability Basic Profile 1.0 and makes use of executable business process models defined in the Business Process Execution Language (BPEL).This paper discusses the rationale behind the decision for SOA, process choreography, and Web services, and gives an overview of the BPEL-centric process choreography architecture. Furthermore, it features lessons learned and best practices identified during design, implementation, and rollout of the solution.

/pdf/service-oriented-architecture-and-business-process-4flnham6z7.pdf

Service-oriented architecture and business process choreography in an order management scenario: rationale, concepts, lessons learned

Decomposing a software system into smaller parts always has been a challenge in software engineering. It is particularly important to split distributed systems into loosely coupled and highly cohesive units. Service-oriented architectures and their microservices deployments tackle many related problems, but remain vague on how to cut a system into discrete, autonomous, network-accessible services. In this paper, we propose a structured, repeatable approach to service decomposition based on 16 coupling criteria distilled from the literature and industry experience. These coupling criteria form the base of Service Cutter, our method and tool framework for service decomposition. In the Service Cutter approach, coupling information is extracted from software engineering artifacts such as domain models and use cases and represented as an undirected, weighted graph to find and score densely connected clusters. The resulting candidate service cuts promise to reduce coupling between and promote high cohesion within services. In our validation activities, which included prototyping, action research and case studies, we successfully decomposed two sample applications with acceptable performance; most (but not all) test scenarios resulted in appropriate service cuts. These results as well as early feedback from members of the target audience in industry and academia suggest that our coupling criteria catalog and tool-supported service decomposition approach have the potential to assist a service architect’s design decisions in a viable and practical manner.

/pdf/service-cutter-a-systematic-approach-to-service-49ly8pbwgf.pdf

Olaf Zimmermann

Papers

Restful web services vs. "big"' web services: making the right architectural decision

Microservices tenets

Managing architectural decision models with dependency relations, integrity constraints, and production rules

Service-oriented architecture and business process choreography in an order management scenario: rationale, concepts, lessons learned

Service Cutter: A Systematic Approach to Service Decomposition