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

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

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

The Communication program emphasizes theory, research, and application to examine the ways humans communicate, verbally and non-verbally, across a variety of levels and contexts. This is particularly important as communication shapes our ideas and values, gives rise to our politics, consumption and socialization, and helps to define our identities and realities. Its power and potential is inestimable. From the briefest of text messages to the grandest of public declarations, we indeed live within communication and invite you to join us in appreciating its increasing importance in contemporary society. From Twitter and reality television to family relationships and workplace dynamics, communication is about understanding ourselves, our media, our relationships, our culture and how these things connect.

의사 간호사 communication

The service-oriented modeling and architecture modeling environment (SOMA-ME) is first a framework for the model-driven design of service-oriented architecture (SOA) solutions using the service-oriented modeling and architecture (SOMA) method. In SOMA-ME, Unified Modeling Language (UML™) profiles extend the UML 2.0 metamodel to domain-specific concepts. SOMA-ME is also a tool that extends the IBM Rational® Software Architect product to provide a development environment and automation features for designing SOA solutions in a systematic and model-driven fashion. Extensibility, traceability, variation-oriented design, and automatic generation of technical documentation and code artifacts are shown to be some of the properties of the SOMA-ME tool.

SOMA-ME: a platform for the model-driven design of SOA solutions

A powerful set of features can assist software developers in searching source code. Source code can be queried to find occurrences of source code elements mapped to domain concepts appearing in an ontology. Queries can specify a search for occurrences of particular operations performed on domain concepts within the source code. Query expansion can be used to locate related domain concepts as indicated in the ontology. Query keyword weighting can be used to emphasize one domain concept over another. Tools can be provided to create the ontology and associate the ontology with the elements appearing in the source code. Results can include user interface features assisting in navigation to a location within the source code associated with the query results.

Semantic-based query techniques for source code

Software developers often face steep learning curves in using a new framework, library, or new versions of frameworks for developing their piece of software. In large organizations, developers learn and explore use of frameworks, rarely realizing, several peers may have already explored the same. A tool that helps locate samples of code, demonstrating use of frameworks or libraries would provide benefits of reuse, improved code quality and faster development. This paper describes an approach for locating common samples of source code from a repository by providing extensions to an information retrieval system. The approach improves the existing approaches in two ways. First, it provides the scalability of an information retrieval system, supporting search over thousands of source code files of an organization. Second, it provides more specific search on source code by preprocessing source code files and understanding elements of the code as opposed to considering code as plain text.

/pdf/using-an-information-retrieval-system-to-retrieve-source-bzif829ng0.pdf

Using an information retrieval system to retrieve source code samples

In an IT service delivery environment, the speedy dispatch of a ticket to the correct resolution group is the crucial first step in the problem resolution process. The size and complexity of such environments make the dispatch decision challenging, and incorrect routing by a human dispatcher can lead to significant delays that degrade customer satisfaction, and also have adverse financial implications for both the customer and the IT vendor. In this paper, we present SmartDispatch, a learning-based tool that seeks to automate the process of ticket dispatch while maintaining high accuracy levels. SmartDispatch comes with two classification approaches - the well-known SVM method, and a discriminative term-based approach that we designed to address some of the issues in SVM classification that were empirically observed. Using a combination of these approaches, SmartDispatch is able to automate the dispatch of a ticket to the correct resolution group for a large share of the tickets, while for the rest, it is able to suggest a short list of 3-5 groups that contain the correct resolution group with a high probability. Empirical evaluation of SmartDispatch on data from 3 large service engagement projects in IBM demonstrate the efficacy and practical utility of the approach.

http://wan.poly.edu/KDD2012/docs/p1393.pdf

SmartDispatch: enabling efficient ticket dispatch in an IT service environment

Service Oriented Architecture (SOA) has gained popularity as a design paradigm for realizing enterprise software systems through abstract units of functionality called services While the key design principles of SOA have been discussed at length in the literature, much of the work is prescriptive in nature and do not explain how adherence to these principles can be quantitatively measured in practice In some cases, metrics for a limited subset of SOA quality attributes have been proposed, but many of these measures have not been empirically validated on real-life SOA designs In this paper, we take a deeper look at how the key SOA quality attributes of service cohesion, coupling, reusability, composability and granularity may be evaluated, based only on service design level information We survey related work, adapt some of the well-known software design metrics to the SOA context and propose new measures where needed These measures adhere to mathematical properties that characterize the quality attributes We study their applicability on two real-life SOA design models from the insurance industry using a metrics computation tool integrated with an Eclipse-based service design environment We believe that availability of these measures during SOA design will aid early detection of design flaws, allow different design options and trade-offs to be considered and support planning for development, testing and governance of the services

/pdf/measuring-the-quality-of-service-oriented-design-48o5lkawjb.pdf

Renuka Sindhgatta

Papers

SOMA-ME: a platform for the model-driven design of SOA solutions

Semantic-based query techniques for source code

Using an information retrieval system to retrieve source code samples

SmartDispatch: enabling efficient ticket dispatch in an IT service environment

Measuring the Quality of Service Oriented Design