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

The Foundations of Statistics By Prof. Leonard J. Savage. (Wiley Publications in Statistics.) Pp. xv + 294. (New York; John Wiley and Sons, Inc.; London: Chapman and Hall, Ltd., 1954.) 48s. net.

Probability and Statistical Inference

Zeros of orthogonal polynomials

An objects-first strategy for teaching introductory computer science courses is receiving increased attention from CS educators. In this paper, we discuss the challenge of the objects-first strategy and present a new approach that attempts to meet this challenge. The new approach is centered on the visualization of objects and their behaviors using a 3D animation environment. Statistical data as well as informal observations are summarized to show evidence of student performance as a result of this approach. A comparison is made of the pedagogical aspects of this new approach with that of other relevant work.

/pdf/teaching-objects-first-in-introductory-computer-science-469gvhbn6a.pdf

Teaching objects-first in introductory computer science

At least certain embodiments of the present disclosure include an environment with user interface software interacting with a software application. A method for operating through an application programming interface (API) in this environment includes transferring a set bounce call. The method further includes setting at least one of maximum and minimum bounce values. The set bounce call causes a bounce of a scrolled region in an opposite direction of a scroll based on a region past an edge of the scrolled region being visible in a display region at the end of the scroll.

Application Programming Interfaces for Scrolling Operations

On the minimal degrees of characters of Sn

We describe a way to introduce objects at the beginning of the first CS course through the use of objects that have significant nontrivial behavior and interactions with other objects. We will describe four introductory laboratory projects and an outline for introductory lectures on object oriented programming that illustrate the need for private member data, constructors and accessor member functions, and prepare students for writing object oriented programs in Java with graphical user interfaces.

/pdf/objects-from-the-beginning-with-guis-39e9ig3ubn.pdf

Objects from the beginning - with GUIs

The Java Power Tools or JPT is a Java toolkit designed to enable students to rapidly develop graphical user interfaces in freshman computer science programming projects. Because it is simple to create GUIs using JPT, students can focus on the more fundamental issues of computer science rather than on widget management. In a separate article[4], we will discuss with examples how the JPT can help freshman students to learn about the basics of algorithms, data structures, classes, and interface design. In this article, we will focus on how the JPT itself can be used as an extended case study of object-oriented design principles in a more advanced course.The fundamental design principles of the JPT are that the elements of a graphical user interface should be able to be combined recursively as nested views and that the communication between these views and the internal data models should be as automatic as possible. In particular, in JPT, the totality of user input from a complex view can be easily converted into a corresponding data model and any input errors will be detected and corrected along the way. This ease of communication is achieved by using string objects as a lingua franca for views and models and by using parsing when appropriate to automatically check for errors and trigger recovery. The JPT achieves its power by a combination of computer science and software design principles. Recursion, abstraction, and encapsulation are systematically used to create GUI tools of great flexibility. It should be noted that a much simpler pedagogical package for Java IO was recently presented in [9].

/pdf/java-power-tools-model-software-for-teaching-object-oriented-1av1so1dgu.pdf

Java power tools: model software for teaching object-oriented design

classes should emphasize uniformity and parsimony by encapsulating information that needs to be hidden and by providing information that needs to be communicated. The decision about what needs to be hidden or communicated must be made in the context of the particular software that is being designed. Hiding hedges a designer's bets about what implementation details are likely to change. As [2] points out, it is impossible to have infinite design flexibility and allow for all possible changes. Therefore, abstract classes need to be tuned to a particular programming situation and the design areas most likely to undergo change. This insight has important pedagogical implications. For one thing, it explains why if you examine a dozen textbooks on data structures you will find a dozen variants of an abstract list class. Each author has envisioned the use of the list class within the context of the software in the book and chosen a set of abstractions to communicate and a collection of details to hide. Since each book has a different teaching philosophy, it is no wonder that these list abstractions turn out to be different. As teachers, we should not attempt to present the list class but should rather discuss the variety of ways in which a list might be abstracted to meet specific design flexibility goals. More generally, abstract classes are not the basic unit of reusable software. What is reusable are concrete classes that are complete and relatively open to access and extension. Pedagogically, this means that we must give careful consideration to the design of concrete classes as well as to the building of abstract layers on top of such classes. The implications of the need to tune abstract classes to the design flexibility goals of a particular piece of software is that abstract classes had better be easy to build. To accomplish this, the member functions in an abstract class must have simple implementations. Ideally, each member function in an abstract class should be implemented by at most three lines of code that refers to some underlying concrete object: setup, make-concrete-call, cleanup. Degrees of Abstraction and Practical Issues Of course, in a practical world (both in industry and in the classroom), we do not always do what is ideal. There is a great temptation to design an abstract interface and then I December 1997 5 Vol. 29 No. 4 inroads SIGCSE Bulletin code substantial algorithmic details into the implementation. However, to the degree that an abstract class does concrete algorithmic work, it ceases to be abstract. If an abstract class does concrete algorithmic work then it will not be easy to change these implementation details and the benefits of abstraction will be lost. At the same time, the algorithmic work will be hidden by an interface which fails to deliver its full potential to the caller. Hence, when the algorithm is needed again in a different context, the class wrapper may have to be redesigned anyway. Let's name this phenomenon. We will call a class quasi-abstract if it has an abstract interface but has an implementation which directly codes substantial algorithmic details. Many examples used in texts for abstract classes are really more precisely quasi-abstract classes. This means that the data representation is hidden properly, but that the algorithmic work has been done inline rather than being similarly encapsulated. To some degree, such a class is a design compromise and should be recognized as such in a teaching setting. Recognition of this compromise is crucial to dealing with it from a design perspective. As educators, the reason that we tend to drift into quasi-abstract classes is twofold. On one hand, we do not have a robust enough set of concrete classes to build upon. On the other hand, to create a truly abstract class, we may need to add another level of abstraction and the pain of additional names and class definitions is too much to bear. Certainly, in the context of busy courses, there is sometimes not enough time to do everything in the best possible way. Nevertheless, we should try to set design standards and, when we need to compromise, we should be explicit about what we are doing and why. Teaching is about being aware. Toolkits and Environments If we wish to teach design then we must work in an environment that is rich in tools. As Eric Roberts states [10]: Students need exposure to modem software development practices. It is no longer acceptable to teach programming by having students write small, self-contained programs from scratch. Even though we cannot predict exactly what toolkits and programming environments students will use when they graduate, it is essential that they learn to work with some modem programming environment that forces them to add small amounts of special-purpose code on top of a large existing infrastructure. Moreover, this experience should come early in the curriculum so that students can apply these techniques in summer internships and their more advanced courses. In addition to reflecting current practice, there are important pedagogical reasons for students to work within a framework of existing code, tools, and class structures. The most important reason for asking students to extend or modify existing code is to permit them to see and explore a large base of well designed code. If we provide a partial solution to an assignment, we have the opportunity to model the kind of software design principles we preach. If we provide a set of tools to be used in a number of assignments, we have a chance to show what we mean by quality reusable software. The code we deliver to students is our stake in the ground about what quality design means in practice. As students progress in the curriculum, they must be exposed to large scale commercial development environments. Although source code is usually not available in such environments, students can still learn by studying the pros and cons of the class libraries provided in the environment. A large scale class library may be used as a set of examples against which the design methodologies in textbooks can be examined. Students can learn about the strengths and weaknesses of both the libraries and the methodologies. It must be recognized that it will not be easy to have faculty everywhere use framework code, toolkits, and advanced environments. Few faculty have the support structure necessary to build large quantities of code. Those of us who have designed and built such pedagogical code must make every effort to distribute this work to faculty at other institutions. However, due to portability issues, this distribution task is not easy. Therefore, it will take time for good code and lab depositories to be put into place. The main obstacle to the faculty use of large scale commercial development environments is the time it takes to grasp the class libraries. With documents that are several inches thick, it must be considered a non-trivial task for faculty to learn an environment well enough to use it in teaching. Hopefully, CS units will begin to allow time for their faculty to pursue this learning process. Design and the Software Project Course In most institutions, students take a junior/senior project course in which they learn about software design and development in a team setting. Ideally, this course should provide a situation in which the design principles learned in earlier courses are put into practice and new design principles applicable to large scale projects are taught. Unfortunately, it is often the case that the project course is a negative design experience. The course is often treated as a rite-of-passage in which students are expected to work ridiculously long hours in order to finish a project on time. SIGCSE Bulletin i n r o a d s Vol. 29 No. 4 6 December 1997 Under these circumstances, there is no incentive for students to ponder a design, explore alternatives, and backtrack to make improvements. Just as in the freshman courses, solving the problem is enough however ugly the code or the design. Of course, students know that there is some penalty for poor design but they also know that the biggest penalty is for not having a running program that roughly meets the specs. We believe that the project course must cease to be a rite-of-passage and instead must become a serious investigation of design issues. Using an incremental approach to building software, students should be encouraged to add class structures and then build working partial solutions every 2-3 days ifpossible. If students "fall behind" in terms of meeting the most ambitious project goals, they should be permitted to "make up the grade" by explaining in writing what design pathways they explored and why their design is of higher quality because of this exploration. The grades will then depend not only on how much the students did but also on what they learned along the way. Design is ultimately a human process and, beyond a reasonable level, stress is counter-productive for the creative work that design requires. If we believe that it is possible to have both rapid development and well designed software [3], then we must use the project course to teach students how this can be accomplished. We must be models of best practice and we must not encourage the mistakes so often committed in industry. We need to make the project course a calm, humane, creative, and rational design experience. Our students will then be able to bring to their future work both good methodologies and good attitudes.

Design issues in computer science education

At educational computer conferences and exhibits, one is overwhelmed by the extensive use of computers as learning tools in almost any subject. However, the one subject which stands out for its limited use of computers is computer science. There is a tendency in computer science education to focus on what goes on in the mind: design and analysis of algorithms, development of data structures, and use of abstraction and modularization in programming. The computer is seen only as the object of study but not as a tool in the educational process. The danger in such an approach to teaching computer science is that the student may become facile in the use of formalism and languages but may fail to deeply grasp what is really going on One important way in which computers have been used in computer science to assist the educational process is by the development of algorithm animations. Algorithm animations have been created by a number of computer science educators including ourselves [2,3,4,5,6,7,8, 10,11, 12], The primary goals of these algorithm animations have been: .

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Richard Rasala

Papers

On the minimal degrees of characters of Sn

Objects from the beginning - with GUIs

Java power tools: model software for teaching object-oriented design

Design issues in computer science education

From animation to analysis in introductory computer science