National Institute of Standards and Technology における超伝導研究及び生活

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

Will Tracz, our esteemed editor and Used-Program salesman, has written an entertaining, non-technical book dealing with the practice (and lack of) of software reuse. Its a collection of essays, mostly rehashed (reused?) and updated from various columns and papers published over the years.. Its a short (a bit over 200 pages) easy reading and enjoyable book (I read most of it in one sitting). Some of the essays discuss what was printed in the past and a discussion of the current status of the points.

Safeware: System Safety and Computers

In this chapter we provide an overview of the concept of blockchain technology and its potential to disrupt the world of banking through facilitating global money remittance, smart contracts, automated banking ledgers and digital assets. In this regard, we first provide a brief overview of the core aspects of this technology, as well as the second-generation contract-based developments. From there we discuss key issues that must be considered in developing such ledger based technologies in a banking context.

/pdf/understanding-modern-banking-ledgers-through-blockchain-5g6unyl0xr.pdf

Understanding Modern Banking Ledgers Through Blockchain Technologies: Future of Transaction Processing and Smart Contracts on the Internet of Money

Secure engineering is relatively more popular for software development methodologies which use standard SDLC such as waterfall, prototyping, etc. However, for agile methodologies (such as extreme programming, scrum, etc.), the adoption of secure engineering is still a debate among academics and practitioners since it potentially alter the nature of agile. This paper tries to investigate farthest works of the adaptation of secure engineering to agile methods, and to compile best propositions on how to conduct agile software development by still adhering security principles. From the review, addition/recruitment of security engineer into team is the most appreciated, while the modification of agile methodology and project management is not really favored by practitioners. This paper will be a starting point of further study to fill research/knowledge gap which is provided in this paper.

http://ieeexplore.ieee.org/iel7/7176053/7224583/07224607.pdf

Preliminary Investigation

The benefits ascribed to Agile methods are attractive to software engineers working in the safety-critical software domain. There is limited industrial experience and evidence of successful applications of Agile methods in this domain, however, academic research has identified some of the key challenges of their adoption and application, and has started to present feasibility studies. In this paper, we propose an iterative approach for developing safety-critical software, making two novel contributions. Firstly, we address the notion of up-front design in safety-critical software development, and describe the characteristics fan up-front design that is minimal from the perspective of achieving safety objectives. Secondly, we identify a key difficulty of using iterative development for building safety-critical software, and present a way to develop both a software system and a safety argument iteratively. We also give details of a proof-of-concept example illustrating the use of the approach.

An Iterative Approach for Development of Safety-Critical Software and Safety Arguments

A secure system is one that is protected against specific undesired outcomesDelivering a secure system, and particularly a secure web application, is not easyIntegrating general-purpose information systems development methods withsecurity development activities could be a useful means to surmount thesedifficulties Agile processes, such as Extreme Programming, are of increasing interest insoftware development Most significantly for web applications, agile processesencourage and embrace requirements change, which is a desirable characteristicfor web application developmentIn this paper, we present an agile process to deliver secure web applicationsThe contribution of the research is not the development of a new method or processthat addresses security concerns Rather, we investigate general-purpose informationsystemdevelopment methods (eg, Feature-Driven Development (FDD)) and mature security methods, namely risk analysis, and integrate them to address the development of secure web applications The key features of our approach are(1) a process capable of dealing with the key challenges of web applicationsdevelopment, namely decreasing life-cycle times and frequently changing requirements; and (2) an iterative approach to risk analysis that integrates security design throughout the development process

Agile development of secure web applications

The effective provision of security in an agile development requires a new approach: traditional security practices are bound to equally traditional development methods. However, there are concerns that security is difficult to build incrementally, and can prove prohibitively expensive to refactor. This paper describes how to grow security, organically, within an agile project, by using an incremental security architecture which evolves with the code. The architecture provides an essential bridge between system-wide security properties and implementation mechanisms, a focus for understanding security in the project, and a trigger for security refactoring. The paper also describes criteria that allow implementers to recognize when refactoring is needed, and a concrete example that contrasts incremental and ‘top-down' architectures.

Agile security using an incremental security architecture

A key concern in safety engineering is understanding the overall emergent failure behaviour of a system, i.e., behaviour exhibited by the system that is outside its specification of acceptable behaviour. A system can exhibit failure behaviour in many ways, including that from failures of individual or a small number of components. It is important for safety engineers to understand how system failure behaviour relates to failures exhibited by individual components. In this paper, we propose a safety analysis technique, failure propagation and transformation analysis (FPTA), which automatically and quantitatively analyses failures based on a model of failure logic. The technique integrates previous work on automated failure analysis with probabilistic model checking supported by the PRISM tool. We demonstrate the technique and tool on a small, yet realistic safety-related application.

Xiaocheng Ge

Papers

An Iterative Approach for Development of Safety-Critical Software and Safety Arguments

Agile development of secure web applications

Agile security using an incremental security architecture

Probabilistic Failure Propagation and Transformation Analysis

Multipath ultrasonic gas flow-meter based on multiple reference waves.