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

Arthur Chickering is Distinguished Professor of Higher Education at Memphis State University. On leave from the Directorship of the Center for the Study of Higher Education at Memphis State, he is Visiting Professor at George Mason University. Zelda Gamson is a sociologist who holds appointments at the John W. McCormack Institute of Public Affairs at the University of Massachusetts-Boston and in the Center for the Study of Higher and Postsecondary Education at the University of Michigan.

Seven Principles for Good Practice in Undergraduate Education: Faculty Inventory. Institutional Inventory.

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research.

/pdf/introductory-programming-a-systematic-literature-review-3yllvt7k18.pdf

Introductory programming: a systematic literature review

This article uses the improvisational theatre metaphor to examine the performance implications of improvisational processes in firms. We recognize similarities and differences between the concepts of performance and success in both theatre and organizations, and extract three main lessons from improvisational theatre that can be applied to organizational improvisation. In the first lesson, we start by recognizing the equivocal and unpredictable nature of improvisation. The second lesson emphasizes that good improvisational theatre arises because its main focus, in contrast to the focus of firms, is more on the process of improvising and less on the outcomes of improvisation. Lastly, in the third lesson, we look at the theatre techniques of ‘agreement’, ‘awareness’, ‘use of ready-mades’, and ‘collaboration’, and translate them into concepts that are relevant for organizations in developing an improvisational capability.

Theatrical Improvisation: Lessons for Organizations

The Impact of College on Students.

The affective domain can be used to support the internalization of cognitive content and foster the development of curriculum and industry-related interests, attitudes, values, and practices. This study investigated correlations between affective factors and course grade. Interest, perceived competence, effort, lack of pressure, and value correlated significantly with CS1 course grades. Moreover, this study investigated the levels of these factors over the course of CS1. Almost all of the levels of these significant factors decreased significantly during the CS1 course as measured by pretests and posttests. Results of this study further indicated that the use of specific affective objectives and instructional strategies lessened these decreases.

http://www.cis.usouthal.edu/~mckinney/p228-mckinney.pdf

Houston, we have a problem: there's a leak in the CS1 affective oxygen tank

The research on teaching and learning over the past 50 years suggests that the early use of collaborative learning leads to higher interest, higher retention, and higher academic performance in students. Early use of these techniques can also increase the sense of belonging for students and can lead to the early development of collaborative skills to prepare students for team experiences in subsequent courses and future careers. During the weekly lab sessions of a second semester introduction to programming course students engaged in collaborative learning experiences through team-based problem solving, project planning, pair programming, and other agile software development practices. Course objectives provided specific goals and criteria for assessment relative to these skills. The assessment in the authors' prior work identified several problem areas which led to specific initiatives to address those problems: (a) instructor-chosen teams, (b) early instruction and reflection on team skills, (c) feedback on team performance, and (d) the use of an IDE that incorporates an automated test-driven development tool. This paper describes the implementation and assessment of these efforts. A significant increase in student team skills from the middle of the semester to the end of the semester was observed.

http://www.cis.usouthal.edu/~mckinney/fp224-mckinney.pdf

Developing collaborative skills early in the CS curriculum in a laboratory environment

Team experiences can be important learning experiences, and industry highly values team skills in graduates Low retention rates might also be improved with early team experiences because team experiences have been linked to increases in the sense of belonging, a key retention factor Team experiences in upper-level courses could also benefit from earlier team experiences The purpose of this paper is to describe and evaluate an early use of teams in the second semester of our CS1 sequence The instructional methodology drew heavily upon the professional practices of an agile software development model, Extreme Programming These professional practices fostered the development of team skills during a semester-long project in a closed lab The evaluation of our experience revealed aspects of the good, the bad, and the ugly Areas for improvement and future work are also explained

http://www.cis.usouthal.edu/~mckinney/p284-mckinney.pdf

Affective assessment of team skills in agile CS1 labs: the good, the bad, and the ugly

The affective domain can be used to support the internalization of cognitive content and foster the development of curriculum and industry-related interests, attitudes, values, and practices. During a two-year period, using validated instruments, the authors measured student interest, value, effort, perceived competence, lack of pressure, student-peer belonging, and student-faculty belonging. Initial findings included a positive correlation between each affective factor and course grade, a significant decrease in the levels of affective factors over the course term, and a lessening of those decreases with the use of specific affective objectives and instructional strategies. The current study built upon these initial results by incorporating new quantitative and qualitative data for each affective factor. The paper reports on the results of these analyses and offers practical suggestions and instructional guidelines based upon the findings. These findings appear to be broadly applicable throughout our curriculum and could extend to other science, mathematics, engineering, and technology disciplines.

Affective factors and student achievement: a quantitative and qualitative study

University instructors are faced with many challenges, especially in the science and technology fields. Often instructors are not formally trained in educational techniques. Course content is dynamically changing and must be the focus of the instructional effort. This is compounded by the high economic incentives of these disciplines, which continue to drive enrollments upward while still not satisfying industry demand for skilled graduates. The School of Computer and Information Sciences at the University of South Alabama has devoted much time and resources in recent years to address these concerns. With the initial support of NSF -CCD grants, a cognitive-based approach to define introductory computer science courses was established. This approach was based on the explicit use of behavioral objectives of Bloom’s taxonomy. This framework has continued to be used successfully as these courses were adapted to include new paradigms, programming languages, and concepts. These courses still meet the underlying curricular goals and have not required substantial re -invention. New faculty have become involved in these courses with limited oversight by senior faculty who were the original developers. With this transition successfully accomplished, another concern, student motivation, critical and pervasive throughout the learning process, was identified and targeted. The initial grants mention that some factors might be outside of faculty control. However, since the cognitive-based approach is stable, those previous factors, such as motivation, can now be addressed. The work of Maslow provides a framework that can be easily integrated into the existing Bloom cognitive-based framework. Students will be guided toward cognitive and affective-based rewards with a vocabulary and reflective framework consistent with Maslow. Through reflection, students will anticipate and recognize how the achievement of specific goals satisfies deeper objectives in their overall intellectual growth and self -actualization. By participating in self -reflection, students will be able to carry associated self -regulated learning habits into later courses and achieve a more active role in their learning experiences. While at first being an expected behavior that is prompte d by the educational setting, motivated learning should ultimately become an internalized life -habit. This approach prepares students to embark on careers that require the practice of lifelong learning. This effort to address the motivation factor should not disturb but rather extend, complement, and complete our prior work.

/pdf/integrated-use-of-bloom-and-maslow-for-instructional-success-3uiatrputd.pdf

Leo F. Denton

Papers

Houston, we have a problem: there's a leak in the CS1 affective oxygen tank

Developing collaborative skills early in the CS curriculum in a laboratory environment

Affective assessment of team skills in agile CS1 labs: the good, the bad, and the ugly

Affective factors and student achievement: a quantitative and qualitative study

Integrated Use of Bloom and Maslow for Instructional Success in Technical and Scientific Fields