THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

This sales letter may not influence you to be smarter, but the book that we offer will evoke you to be smarter. Yeah, at least you'll know more than others who don't. This is what called as the quality life improvisation. Why should this research methods in social relations? It's because this is your favourite theme to read. If you like this theme about, why don't you read the book to enrich your discussion?

Research methods in social relations

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

A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.

Multiobjective evolutionary algorithms: A survey of the state of the art

Cost estimation is a vital task in most important software project decisions such as resource allocation and bidding. Analogy-based cost estimation is particularly transparent, as it relies on historical information from similar past projects, whereby similarities are determined by comparing the projects' key attributes and features. However, one crucial aspect of the analogy-based method is not yet fully accounted for: the different impact or weighting of a project's various features. Current approaches either try to find the dominant features or require experts to weight the features. Neither of these yields optimal estimation performance. Therefore, we propose to allocate separate weights to each project feature and to find the optimal weights by extensive search. We test this approach on several real-world data sets and measure the improvements with commonly used quality metrics. We find that this method 1) increases estimation accuracy and reliability, 2) reduces the model's volatility and, thus, is likely to increase its acceptance in practice, and 3) indicates upper limits for analogy-based estimation quality as measured by standard metrics.

/pdf/optimal-project-feature-weights-in-analogy-based-cost-2zew3vvkf1.pdf

Optimal project feature weights in analogy-based cost estimation: improvement and limitations

A 2002 survey found that many companies use software reviews unsystematically, creating a mismatch between expected outcomes and review implementations. This suggests that many software practitioners understand basic review concepts but often fall to exploit their full potential.

Software reviews, the state of the practice

Dealing with temporal uncertainties is a key issue in domains like project management or medical treatment planning. However, support for temporal indeterminacies is not very well integrated in current methods, techniques, and tools. In this paper, we present a visualization technique called PlanningLines that allows for representing temporal uncertainties and aims at supporting project managers in their difficult planning and controlling tasks. We conducted a controlled experiment to gather empirical evidence on the strengths and limitations of our approach. Main results are that PlanningLine users make fewer mistakes and are faster in conducting tasks than users of a traditional visualization technique.

/pdf/planninglines-novel-glyphs-for-representing-temporal-ljjfmghvvn.pdf

PlanningLines: novel glyphs for representing temporal uncertainties and their evaluation

Inspection is an effective but also expensive quality assurance activity to find defects early during software development. The defect detection process, team size, and staff hours invested can have a considerable impact on the defect detection effectiveness and cost-benefit of an inspection. In this paper, we use empirical data and a probabilistic model to estimate this impact for nominal (noncommunicating) inspection teams in an experiment context. Further, the analysis investigates how cutting off the inspection after a certain time frame would influence inspection performance. Main findings of the investigation are: 1) Using combinations of different reading techniques in a team is considerably more effective than using the best single technique only (regardless of the observed level of effort). 2) For optimizing the inspection performance, determining the optimal process mix in a team is more important than adding an inspector (above a certain team size) in our model. 3) A high level of defect detection effectiveness is much more costly to achieve than a moderate level since the average cost for the defects found by the inspector last added to a team increases more than linearly with growing effort investment. The work provides an initial baseline of inspection performance with regard to process diversity and effort in inspection teams. We encourage further studies on the topic of time usage with defect detection techniques and its effect on inspection effectiveness in a variety of inspection contexts to support inspection planning with limited resources.

Investigating the defect detection effectiveness and cost benefit of nominal inspection teams

Project managers aim at keeping track of interdependencies between various artifacts of the software development lifecycle, to find out potential requirements conflicts, to better understand the impact of change requests, and to fulfill process quality standards, such as CMMI requirements. While there are many methods and techniques on how to technically store requirements traces, the economic issues of dealing with requirements tracing complexity remain open. In practice tracing is typically not an explicit systematic process, but occurs rather ad hoc with considerable hidden tracing-related quality costs. This paper reports a case study on value-based requirements tracing (VBRT) that systematically supports project managers in tailoring requirements tracing precision and effort based on the parameters stakeholder value, requirements risk/volatility, and tracing costs. Main results of the case study were: (a) VBRT took around 35% effort of full requirements tracing; (b) more risky or volatile requirements warranted more detailed tracing because of their higher change probability.

/pdf/a-case-study-on-value-based-requirements-tracing-5bcypmp993.pdf

Stefan Biffl

Papers

Optimal project feature weights in analogy-based cost estimation: improvement and limitations

Software reviews, the state of the practice

PlanningLines: novel glyphs for representing temporal uncertainties and their evaluation

Investigating the defect detection effectiveness and cost benefit of nominal inspection teams

A case study on value-based requirements tracing