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

Microservices is an architectural style inspired by service-oriented computing that has recently started gaining popularity. Before presenting the current state of the art in the field, this chapter reviews the history of software architecture, the reasons that led to the diffusion of objects and services first, and microservices later. Finally, open problems and future challenges are introduced. This survey primarily addresses newcomers to the discipline, while offering an academic viewpoint on the topic. In addition, we investigate some practical issues and point out a few potential solutions.

/pdf/microservices-yesterday-today-and-tomorrow-30wvjeeix0.pdf

Microservices: Yesterday, Today, and Tomorrow

A novel paradigm that changes the scene for the modern communication and computation systems is the Edge Computing. It is not a coincidence that terms like Mobile Cloud Computing, Cloudlets, Fog Computing, and Mobile-Edge Computing are gaining popularity both in academia and industry. In this paper, we embrace all these terms under the umbrella concept of “Edge Computing” to name the trend where computational infrastructures hence the services themselves are getting closer to the end user. However, we observe that bringing computational infrastructures to the proximity of the user does not magically solve all technical challenges. Moreover, it creates complexities of its own when not carefully handled. In this paper, these challenges are discussed in depth and categorically analyzed. As a solution direction, we propose that another major trend in networking, namely software-defined networking (SDN), should be taken into account. SDN, which is not proposed specifically for Edge Computing, can in fact serve as an enabler to lower the complexity barriers involved and let the real potential of Edge Computing be achieved. To fully demonstrate our ideas, initially, we put forward a clear collaboration model for the SDN-Edge Computing interaction through practical architectures and show that SDN related mechanisms can feasibly operate within the Edge Computing infrastructures. Then, we provide a detailed survey of the approaches that comprise the Edge Computing domain. A comparative discussion elaborates on where these technologies meet as well as how they differ. Later, we discuss the capabilities of SDN and align them with the technical shortcomings of Edge Computing implementations. We thoroughly investigate the possible modes of operation and interaction between the aforementioned technologies in all directions and technically deduce a set of “Benefit Areas” which is discussed in detail. Lastly, as SDN is an evolving technology, we give the future directions for enhancing the SDN development so that it can take this collaboration to a further level.

How Can Edge Computing Benefit From Software-Defined Networking: A Survey, Use Cases, and Future Directions

The accelerating progress of network speed, reliability and security creates an increasing demand to move software and services from being stored and processed locally on users' machines to being managed by third parties that are accessible through the network. This has created the need to develop new software development methods and software architectural styles that meet these new demands. One such example in software architectural design is the recent emergence of the microservices architecture to address the maintenance and scalability demands of online service providers. As microservice architecture is a new research area, the need for a systematic mapping study is crucial in order to summarise the progress so far and identify the gaps and requirements for future studies. In this paper we present a systematic mapping study of microservices architectures and their implementation. Our study focuses on identifying architectural challenges, the architectural diagrams/views and quality attributes related to microsevice systems.

/pdf/a-systematic-mapping-study-in-microservice-architecture-28se10o7ki.pdf

A Systematic Mapping Study in Microservice Architecture

Microservices is an architectural style increasing in popularity. However, there is still a lack of understanding how to adopt a microservice-based architectural style. We aim at characterizing different microservice architectural style patterns and the principles that guide their definition. We conducted a systematic mapping study in order to identify reported usage of microservices and based on these use cases extract common patterns and principles. We present two key contributions. Firstly, we identified several agreed microservice architecture patterns that seem widely adopted and reported in the case studies identified. Secondly, we presented these as a catalogue in a common template format including a summary of the advantages, disadvantages, and lessons learned for each pattern from the case studies. We can conclude that different architecture patterns emerge for different migration, orchestration, storage and deployment settings for a set of agreed principles.

/pdf/architectural-patterns-for-microservices-a-systematic-15eavinkr9.pdf

Architectural Patterns for Microservices: A Systematic Mapping Study

Cloud Computing and Mobile Cloud Computing are reshaping the way applications are being developed and deployed due to their unique needs such as massive scalability, guaranteed fault tolerance, near zero downtime, etc. and also daunting challenges such as security, reliability, continuous deployment and update capability. Microservices architecture, where application is composed of a set of independently deployable services, is increasingly becoming popular due to its capability to address most of these needs and challenges. In recent years, the Behavior-Driven Development (BDD) has become one of the most popular agile software development processes, and frequently used in microservices development. The key to success of BDD is the executable acceptance tests that describe the expected behavior of a feature and its acceptance criteria in the form of scenarios using simple and business people readable syntax. The reusability, auditability, and maintainability become some of the major concerns when BDD test framework is applied for each microservice repository and no previous research addresses these concerns. In this paper, we present a reusable automated acceptance testing architecture to address all these concerns.

A Reusable Automated Acceptance Testing Architecture for Microservices in Behavior-Driven Development

The explosive growth of cloud computing in recent years has led to a massive increase in both the amount of traffic and the number of service requests to cloud servers. This growth trend of load poses serious challenges to the cloud load balancer in efficient balancing of the load, already a daunting job. The cloud load balancing is a highly researched field where numerous solutions to balance load have been proposed. Unfortunately, no research papers provided a comprehensive review focusing Load Balancer as a Service (LBaaS) model. In this paper, we first understand the concepts of load balancing, its importance and desired characteristics in cloud. Then we provide complete review on the existing load balancing strategies, their strength, shortcomings and a comparative study. Finally, we presented load balancer as a service model adopted by the major market players, and our observation, future needs and challenges.

https://www.computer.org/csdl/pds/api/csdl/proceedings/download-article/12OmNxwENOX/pdf

Load Balancer as a Service in Cloud Computing

Green computing and energy-saving have been important and hot research topics in mobile computing and cloud computing. In the recent years, many existing research results have been published to address energy saving issues and challenges in cloud computing or mobile computing. Today, the significant increase of mobile users and strong business interests in cloud computing and services drive an emergent opportunities and needs in mobile cloud computing. This brings a new attention to energy saving in mobile cloud computing and services. This paper first presents the research scope and classified issues in energy saving in mobile clouds. Then, it reviews the existing research results and techniques, and examines their strengths and weaknesses. Finally, the paper offers observations, and identifies the open issues and needs for future research.

Energy Saving in Mobile Cloud Computing

The proliferation of distributed microservices driven by service oriented architecture and the effectiveness of agile software development processes such as Test Driven Development (TDD), Behavior Driven Development (BDD), inspired by extreme programming (XP), have driven the challenging necessity to receive prompt test feedback during software development iteratively Although few test frameworks can execute unit tests in memory in parallel, no existing test frameworks can reliably perform parallel execution for the tests that rely on file system access, database fixtures and network communication Isolated test execution environment with dedicated resources is a prerequisite to address these challenges No previous research addresses this need to run groups of tests in parallel on the same development host In this paper, to cut down test execution time, we present a service framework for parallel tests execution in a developer's containerized sandbox using operating system level virtualization provided by Docker, the new hot driver for Platform as a Service (PaaS)

Mazedur Rahman

Papers

A Reusable Automated Acceptance Testing Architecture for Microservices in Behavior-Driven Development

Load Balancer as a Service in Cloud Computing

Energy Saving in Mobile Cloud Computing

A Service Framework for Parallel Test Execution on a Developer's Local Development Workstation