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

Group Process and Productivity

ERP systems are typically the largest, most complex, and most demanding information systems implemented by firms, representing a major departure from the individual and departmental information systems prevalent in the past. Firms and individuals are extensively impacted, and many problematic issues remain to be researched. ERP and related integrated technologies are a transformative force on the accounting profession. As the nature of business evolves, accounting expertise is being called on to make broader contributions such as reporting on nonfinancial measures, auditing information systems, implementing management controls within information systems, and providing management consulting services. This review of ERP research is drawn from an extensive examination of the breadth of ERP-related literature without constraints as to a narrow timeframe or limited journal list, although particular attention is directed to the leading journals in information systems and accounting information systems...

A Review of ERP Research: A Future Agenda for Accounting Information Systems

The objective of this paper is to present the current evidence relative to the effectiveness of pair programming (PP) as a pedagogical tool in higher education CS/SE courses. We performed a systematic literature review (SLR) of empirical studies that investigated factors affecting the effectiveness of PP for CS/SE students and studies that measured the effectiveness of PP for CS/SE students. Seventy-four papers were used in our synthesis of evidence, and 14 compatibility factors that can potentially affect PP's effectiveness as a pedagogical tool were identified. Results showed that students' skill level was the factor that affected PP's effectiveness the most. The most common measure used to gauge PP's effectiveness was time spent on programming. In addition, students' satisfaction when using PP was overall higher than when working solo. Our meta-analyses showed that PP was effective in improving students' grades on assignments. Finally, in the studies that used quality as a measure of effectiveness, the number of test cases succeeded, academic performance, and expert opinion were the quality measures mostly applied. The results of this SLR show two clear gaps in this research field: 1) a lack of studies focusing on pair compatibility factors aimed at making PP an effective pedagogical tool and 2) a lack of studies investigating PP for software design/modeling tasks in conjunction with programming tasks.

https://researchbank.swinburne.edu.au/file/0de976cc-8dd1-4337-855f-3a95bbe71eb0/1/PDF (Accepted manuscript).pdf

Empirical Studies of Pair Programming for CS/SE Teaching in Higher Education: A Systematic Literature Review

A total of 295 junior, intermediate, and senior professional Java consultants (99 individuals and 98 pairs) from 29 international consultancy companies in Norway, Sweden, and the UK were hired for one day to participate in a controlled experiment on pair programming. The subjects used professional Java tools to perform several change tasks on two alternative Java systems with different degrees of complexity. The results of this experiment do not support the hypotheses that pair programming in general reduces the time required to solve the tasks correctly or increases the proportion of correct solutions. On the other hand, there is a significant 84 percent increase in effort to perform the tasks correctly. However, on the more complex system, the pair programmers had a 48 percent increase in the proportion of correct solutions but no significant differences in the time taken to solve the tasks correctly. For the simpler system, there was a 20 percent decrease in time taken but no significant differences in correctness. However, the moderating effect of system complexity depends on the programmer expertise of the subjects. The observed benefits of pair programming in terms of correctness on the complex system apply mainly to juniors, whereas the reductions in duration to perform the tasks correctly on the simple system apply mainly to intermediates and seniors. It is possible that the benefits of pair programming will exceed the results obtained in this experiment for larger, more complex tasks and if the pair programmers have a chance to work together over a longer period of time

https://homepages.dcc.ufmg.br/~figueiredo/disciplinas/2015a/sqm/paper09ivan.pdf

Evaluating Pair Programming with Respect to System Complexity and Programmer Expertise

Agile Software Development methodologies have grown in popularity both among academic researchers and industrial practitioners. Among the various methodologies or practices proposed, pair programming, which is concerned with two programmers collaborating on design, coding and testing, has become a controversial focus of interest. Even though some success stories have been reported with the use of pair-programming in real software development environment, many people remain rather skeptical of the claims on pair-programming productivity. Previous studies in pair programming have only addressed the basic understanding of the productivity of pairs and they have not addressed the variation in productivity between pairs of varying skills and experience, such as between novice-novice and expert-expert. Statistical productivity measurements reported by different researchers also seem to lead to contradictory conclusions. Until now, the literature has not addressed how those results and experiments were related to each other. In this paper, we propose a controlled experiment called repeat-programming which can facilitate the understanding of relationships between human experience and programming productivity. Repeat-programming can be performed when controversial issues in non-traditional programming methodologies and development productivity need to be investigated into. To illustrate how the proposed empirical experiment can put arguable, divisive problems into perspective, we have examined the productivity in pair programming as a case study. With repeat-programming, we are able to (i) better understand why results of previous pair programming control experiments reached different conclusions as to the productivity of pair programming and (ii) most importantly, present a case in which novice-novice pairs against novice solos are much more productive than expert-expert pairs against expert solos.

/pdf/pair-programming-productivity-novice-novice-vs-expert-expert-2wgwfk4vxw.pdf

Pair programming productivity: Novice-novice vs. expert-expert

This paper reports experimental measurements of productivity and quality in pair programming. The work complements Laurie Williams' work on collaborative programming, in which Pair Programming and Solo Programming student groups wrote the same programs and then their activities were measured to investigate productivity, quality, etc. In this paper, Pair and Solo industrial programmer groups are requested to complete algorithm-style aptitude tests so as to observe the capability of solving algorithms in singles and in pairs. So doing is independent of the familiarity of a programming language. Besides, we also take another approach to examining pair programming. A single group of industrial programmers carries alternately out Pair Programming and Solo Programming. All these demonstrate that productivity in pair programming hinges upon algorithm design at all levels from understanding problems and implementing solutions. In addition, we reach similar conclusions to Williams. Our findings indicate that simple design, refactoring, and rapid feedback provide an excellent continuous-design environment for higher productivity in pair programming.

When does a pair outperform two individuals

Many software projects fail, whether failure is measured in terms of budget, schedule, or some other requirement. The causes of such failures are many, but are not always easily recognized. This is not the least due to the human dimension of corporate activities, as spurious or misdiagnosed issues in Enterprise Resource Planning (ERP) projects can take on a life of their own and become a magnet for company politics. This paper reports an industrial case in which the senior management attempted to deal with a troubled ERP implementation (SAP R/3) in an international fast moving consumer goods (FMCG) company during 2001 and 2002. This paper reflects this dimension as it uses original emails and PowerPoint slides to recount a number of representative episodes in a troubled but ultimately successful project. At the heart of this success is the realization that whereas it can be difficult and time-consuming to do root-cause analyses, it is relatively simple to identify problem owners. In this case, the senior management without IT backgrounds turned around a failing project by reorganizing the team structure according to process areas so that issues in each process area had one problem owner. We summarize the management's actions into a troubleshooting framework, and in addition, suggest three actions for rescuing troubled projects: keep the project manager but narrow down the manager's scope of responsibility to one or two process areas; assign the right people to be responsible for other process areas; and have the General Manager chair the ERP meetings.

Rescuing Troubled Software Projects by Team Transformation: A Case Study With an ERP Project

Pair programming involves-two developers simultaneously collaborating with each other on the same programming task to design and code a solution. Algorithm design and its implementation are normally interwoven in that implementation often provides feedback to enhance the design. Previous controlled pair programming experiments did not explore the efficacy of pairs versus individuals in program design-related tasks separately from coding. Variations in programmer skills in a particular language or an integrated development environment and the understanding of programming instructions can mask the skill of subjects in program design-related tasks. Programming aptitude tests (PATs) have been shown to correlate with programming performance. PATs do not require understanding of programming instructions and do not require a skill in any specific computer language. Two controlled experiments were conducted, with full-time professional programmers being the subjects who worked on increasingly complex programming aptitude tasks related to problem solving and algorithmic design. In both experiments, pairs significantly outperformed individuals, providing evidence of the value of pairs in program design-related tasks.

The Effect of Pairs in Program Design Tasks

Developing areas in China are attracting increasing investment in manufacturing. This has increased the local demand for software and, consequently, demands on local software teams. Such teams, typically small, inexperienced and suffer high personnel turnover, often produce defective products. As software process improvement models are unsuitable for such teams, research was conducted applying a test-driven development (TDD) approach. TDD quickly improved the overall team performance. Our findings are applicable in other Asian developing countries.

Kim Man Lui

Papers

Pair programming productivity: Novice-novice vs. expert-expert

When does a pair outperform two individuals

Rescuing Troubled Software Projects by Team Transformation: A Case Study With an ERP Project

The Effect of Pairs in Program Design Tasks

Test Driven Development and Software Process Improvement in China