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

Internet of Things: A survey on machine learning-based intrusion detection approaches

Spend your time even for only few minutes to read a book. Reading a book will never reduce and waste your time to be useless. Reading, for some people become a need that is to do every day such as spending time for eating. Now, what about you? Do you like to read a book? Now, we will show you a new book enPDFd common knowledge how companies thrive by sharing what they know that can be a new way to explore the knowledge. When reading this book, you can get one thing to always remember in every reading time, even step by step.

Common Knowledge: How Companies Thrive by Sharing What They Know

With the development of technologies, such as big data, cloud computing, and the Internet of Things (IoT), digital twin is being applied in industry as a precision simulation technology from concept to practice. Further, simulation plays a very important role in the healthcare field, especially in research on medical pathway planning, medical resource allocation, medical activity prediction, etc. By combining digital twin and healthcare, there will be a new and efficient way to provide more accurate and fast services for elderly healthcare. However, how to achieve personal health management throughout the entire lifecycle of elderly patients, and how to converge the medical physical world and the virtual world to realize real smart healthcare, are still two key challenges in the era of precision medicine. In this paper, a framework of the cloud healthcare system is proposed based on digital twin healthcare (CloudDTH). This is a novel, generalized, and extensible framework in the cloud environment for monitoring, diagnosing and predicting aspects of the health of individuals using, for example, wearable medical devices, toward the goal of personal health management, especially for the elderly. CloudDTH aims to achieve interaction and convergence between medical physical and virtual spaces. Accordingly, a novel concept of digital twin healthcare (DTH) is proposed and discussed, and a DTH model is implemented. Next, a reference framework of CloudDTH based on DTH is constructed, and its key enabling technologies are explored. Finally, the feasibility of some application scenarios and a case study for real-time supervision are demonstrated.

A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin

Over the past few decades, we have witnessed a dramatic rise in life expectancy owing to significant advances in medical science and technology, medicine as well as increased awareness about nutrition, education, and environmental and personal hygiene. Consequently, the elderly population in many countries are expected to rise rapidly in the coming years. A rapidly rising elderly demographics is expected to adversely affect the socioeconomic systems of many nations in terms of costs associated with their healthcare and wellbeing. In addition, diseases related to the cardiovascular system, eye, respiratory system, skin and mental health are widespread globally. However, most of these diseases can be avoided and/or properly managed through continuous monitoring. In order to enable continuous health monitoring as well as to serve growing healthcare needs; affordable, non-invasive and easy-to-use healthcare solutions are critical. The ever-increasing penetration of smartphones, coupled with embedded sensors and modern communication technologies, make it an attractive technology for enabling continuous and remote monitoring of an individual’s health and wellbeing with negligible additional costs. In this paper, we present a comprehensive review of the state-of-the-art research and developments in smartphone-sensor based healthcare technologies. A discussion on regulatory policies for medical devices and their implications in smartphone-based healthcare systems is presented. Finally, some future research perspectives and concerns regarding smartphone-based healthcare systems are described.

https://www.mdpi.com/1424-8220/19/9/2164/pdf

Smartphone Sensors for Health Monitoring and Diagnosis.

To make microtask programming more efficient and reduce the potential for conflicts between contributors, I developed a new behavior-driven approach to microtasking programming. In our approach, each microtask asks developers to identify a behavior behavior from a high-level description of a function, implement a unit test for it, implement the behavior, and debug it. It enables developers to work on functions in isolation through high-level function descriptions and stubs. 
In addition, I developed the first approach for building microservices through microtasks. Building microservices through microtasks is a good match because our approach requires a client to first specify the functionality the crowd will create through an API. This API can then take the form of a microservice description. A traditional project may ask a crowd to implement a new microservice by simply describing the desired behavior in a API and recruiting a crowd. We implemented our approach in a web-based IDE, \textit{Crowd Microservices}. It includes an editor for clients to describe the system requirements through endpoint descriptions as well as a web-based programming environment where crowd workers can identify, test, implement, and debug behaviors. The system automatically creates, manages, assigns microtasks. After the crowd finishes, the system automatically deploys the microservice to a hosting site.

Large-Scale Microtask Programming

A controlled experiment on the impact of microtasking on programming

A key goal of software engineering research is to improve the environments, tools, languages, and techniques programmers use to efficiently create quality software. Successfully designing these tools and demonstrating their effectiveness involves engaging with tool users — software engineers. Researchers often want to conduct user studies of software engineers to collect direct evidence. However, running user studies can be difficult, and researchers may lack solution strategies to overcome the barriers, so they may avoid user studies. To understand the challenges researchers face when conducting programmer user studies, we interviewed 26 researchers. Based on the analysis of interview data we contribute: (i) a taxonomy of 18 barriers researchers encounter; (ii) 23 solution strategies some researchers use to address 8 of the 18 barriers in their own studies; and (iii) 4 design ideas, which we adapted from the behavioral science community, that may lower 8 additional barriers. To validate the design ideas, we held an in-person all-day focus group with 16 researchers.

/pdf/whats-not-working-in-programmer-user-studies-f83h3z16.pdf

What’s (not) Working in Programmer User Studies?

Key to the effectiveness of crowdsourcing approaches for software engineering is workflow design, describing how complex work is organized into small, relatively independent microtasks. In this paper, we introduce a Behavior-Driven Development (BDD) workflow for accomplishing programming work through self-contained microtasks. In our approach, a client, acting on behalf of a software team, describes a microservice as a set of endpoints with paths, requests, and responses. A crowd then implements the endpoints, identifying individual endpoint behaviors which they test, implement, and debug, creating new functions and interacting with persistence APIs as needed. To evaluate our approach, we conducted a feasibility study in which a small crowd worked to implement a small todo microservice. The crowd created an implementation with only four defects, completing 350 micro- tasks and implementing 13 functions. We discuss the implications of these findings for incorporating crowdsourced programming contributions into traditional software projects.

Crowdsourced Behavior-Driven Development

Key to the effectiveness of crowdsourcing approaches for software engineering is workflow design, describing how complex work is organized into small, relatively independent microtasks. In this paper, we introduce a Behavior-Driven Development (BDD) workflow for accomplishing programming work through self-contained microtasks, implemented as a preconfigured environment called Crowd Microservices. In our approach, a client, acting on behalf of a software team, describes a microservice as a set of endpoints with paths, requests, and responses. A crowd then implements the endpoints, identifying individual endpoint behaviors which they test, implement, and debug, creating new functions and interacting with persistence APIs as needed. To evaluate our approach, we conducted a feasibility study in which a small crowd worked to implement a small ToDo microservice. The crowd created an implementation with only four defects, completing 350 microtasks and implementing 13 functions. We discuss the implications of these findings for incorporating crowdsourced programming contributions into traditional software projects.

Emad Aghayi

Papers

Large-Scale Microtask Programming

A controlled experiment on the impact of microtasking on programming

What’s (not) Working in Programmer User Studies?

Crowdsourced Behavior-Driven Development

Crowdsourced Behavior-Driven Development: Implementing Microservices through Microtasks.