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

[서평]「The Unified Modeling Language User Guide」

Seeking meaning: A process approach to library and information services

Background: The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs, and improve the quality of software. Objective: We investigate how the context of models, the independent variables used, and the modeling techniques applied influence the performance of fault prediction models. Method: We used a systematic literature review to identify 208 fault prediction studies published from January 2000 to December 2010. We synthesize the quantitative and qualitative results of 36 studies which report sufficient contextual and methodological information according to the criteria we develop and apply. Results: The models that perform well tend to be based on simple modeling techniques such as Naive Bayes or Logistic Regression. Combinations of independent variables have been used by models that perform well. Feature selection has been applied to these combinations when models are performing particularly well. Conclusion: The methodology used to build models seems to be influential to predictive performance. Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensively.

/pdf/a-systematic-literature-review-on-fault-prediction-50xbihfieh.pdf

A Systematic Literature Review on Fault Prediction Performance in Software Engineering

This survey covers hard real-time scheduling algorithms and schedulability analysis techniques for homogeneous multiprocessor systems. It reviews the key results in this field from its origins in the late 1960s to the latest research published in late 2009. The survey outlines fundamental results about multiprocessor real-time scheduling that hold independent of the scheduling algorithms employed. It provides a taxonomy of the different scheduling methods, and considers the various performance metrics that can be used for comparison purposes. A detailed review is provided covering partitioned, global, and hybrid scheduling algorithms, approaches to resource sharing, and the latest results from empirical investigations. The survey identifies open issues, key research challenges, and likely productive research directions.

/pdf/a-survey-of-hard-real-time-scheduling-for-multiprocessor-313o35g6ho.pdf

A survey of hard real-time scheduling for multiprocessor systems

As the number of cloud users are increasing, it becomes essential for cloud service providers to allocate the right amount of resources to virtual machines, especially during live migration. In order to increase the resource utilization and reduce waste, the providers have started to think about the role of over-allocating the resources. However, the benefits of over-allocations are not without inherent risks. In this paper, we conducted an experiment using a large telecommunication application that runs inside virtual machines, here we have varied the number of vCPU resources allocated to these virtual machines in order to find the best choice which at the same time reduces the risk of underallocating resources after the migration and increases the performance during the live migration. During our measurements we have used VMware's vMotion to migrate virtual machines while they are running. The results of this study will help virtualized environment service providers to decide how much resources should be allocated for better performance during live migration as well as how much resource would be required for a given load.

Performance Implications of Resource Over-Allocation During the Live Migration

To change applications to fit the needs for users, for different places and for development over time has long been a challenge called software maintenance. In this chapter we take up tailoring as a means to make software flexible. Starting with two case studies - one taking up tailoring for different users and one addressing changes over time - the article discusses the problems with both use and development of a tailorable application. To develop tailorable software raise new challenges; how to make a user friendly tailoring interface, how to decide what should be tailorable and how to make a software architecture that permits this, how to make the tailorable system having an acceptable performance. Our experiences also show that the boarders between maintenance and use become vague since tailorability can replace maintenance by professional software engineers with tailoring by advanced users. Based on the experiences from our two cases, we identify and discuss five important issues when designing and implementing tailorable systems in industrial settings.

/pdf/when-maintenance-becomes-use-experiences-from-two-industrial-2l2rr8owou.pdf

When Maintenance Becomes Use Experiences from two industrial cases of EUD

Chapter 7: Memory modules

The importance of mobile and electronic commerce results in much attention given to credit-control systems. There are high non-functional requirements on such systems, e.g. high availability and reliability. These requirements can be met by changing the architecture of the credit-control system. In this paper the authors suggested a new architecture and a number of alternative implementations of credit-control server. By quantifying the availability, reliability, performance and cost the designers enabled to make better trade-off decisions. The new architecture was compared with the current, "state-of-the-art" solution. Finally, suggestions for the system developers concerning the choice of an appropriate architecture implementation variant were presented.

A cost-efficient server architecture for real-time credit-control

In this study, we evaluate the performance of trajectory queries that are handled by Cassandra, MongoDB, and PostgreSQL. The evaluation is conducted on a multiprocessor and a cluster. Telecommunication companies collect a lot of data from their mobile users. These data must be analysed in order to support business decisions, such as infrastructure planning. The optimal choice of hardware platform and database can be different from a query to another. We use data collected from Telenor Sverige, a telecommunication company that operates in Sweden. These data are collected every five minutes for an entire week in a medium sized city. The execution time results show that Cassandra performs much better than MongoDB and PostgreSQL for queries that do not have spatial features. Statio’s Cassandra Lucene index incorporates a geospatial index into Cassandra, thus making Cassandra to perform similarly as MongoDB to handle spatial queries. In four use cases, namely, distance query, k-nearest neigbhor query, range query, and region query, Cassandra performs much better than MongoDB and PostgreSQL for two cases, namely range query and region query. The scalability is also good for these two use cases.

Lars Lundberg

Papers

Performance Implications of Resource Over-Allocation During the Live Migration

When Maintenance Becomes Use Experiences from two industrial cases of EUD

Chapter 7: Memory modules

A cost-efficient server architecture for real-time credit-control

Performance evaluation of trajector y q ueries on multiprocessor and cluste r