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

Lessons from applying the systematic literature review process within the software engineering domain

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

ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

Software engineering researchers are increasingly relying on the empirical approach to advance the state of the art. The level of empirical rigor and evidence required to guide software engineering research, however, can vary drastically depending on many factors. In this session we identify some of these factors through a discussion of the state of the art in performing empirical studies in software engineering, and we show how we can utilize the notion of empirical maturity to set and adjust the empirical expectations for software engineering research efforts.Regarding the state of the art in performing empirical studies, we will offer perspectives on two classes of study: those concerned with humans utilizing a technology, e.g., a person applying a methodology, a technique, or a tool, where human skills and the ability to interact with the technology are some of the primes issues, and those concerned with the application of the technology to an artifact, e.g., a technique or tool applied to a design or a program. In the first case, the emphasis is typically on issues like feasibility, usefulness, and then on effectiveness. The technology tends to be less well specified and based more on the experience and skills of the technology applier. In the second case, the emphasis is typically on the efficiency and effectiveness of the technology. The technology tends to be well defined and the assumption is that the individual skill and experience plays a less important role. We will discuss the set of factors that influence the design, implementation, and validity of these studies.Regarding empirical maturity and its implications on the SE community's expectations, we will provide examples of the large spectrum of studies with different maturity levels that can be performed to successfully support software engineering research. We will then identify and analyze the following aspects that are likely to impact a study's maturity level: technology (well-specified vs. under development), goals of the study (effectiveness vs. feasibility), type of design and analysis (controlled experiment vs. case study, quantitative vs. qualitative), control and specification of threats to validity (internal vs. external threats), dependence on context (in vivo vs. in vitro), relationship to previous empirical work (replicated on-site, replicated off-site, non-replicated, non-replicable), and purposes of the study (exploratory vs. confirmatory). We will lead a discussion on these key aspects that must be considered to assess the empirical maturity of a piece of work in the context of its research area and the empirical maturity of that area.

Empirically driven SE research: state of the art and required maturity

Numerous reliability models and testing practices are based on operational profiles. Typically, a single operational profile is used to represent the usage of a system with the assumption that a homogeneous customer base executes the system. However, if the customer base is heterogeneous, estimates computed on a single operational profile may be inaccurate. A single operational profile does not reflect the diverse customer patterns and it only "averages" the usage of the system, obscuring the real information about the operations probabilities. Decisions made on these estimates are likely to be biased and of limited usefulness. This paper presents a refinement methodology for the generation of more accurate operational profiles that truly represent the diverse customer usage patterns. Clustering analysis supports the refinement methodology for identifying groups of customers with similar characteristics. Empirical stopping rules and validation procedures complete the refining methodology. A complete example of the methodology is presented on a large application. The example evidences the different perspective and accuracy that can be obtained through this refining methodology.

A methodology for operational profile refinement

This work presents a study of robot software using the Robot Operating System (ROS), focusing on detecting inconsistencies in physical unit manipulation. We discuss how dimensional analysis, the rules governing how physical quantities are combined, can be used to detect inconsistencies in robot software that are otherwise difficult to detect. Using a corpus of ROS software with 5.9M lines of code, we measure the frequency of these dimensional inconsistencies and find them in 6% (211 / 3,484) of repositories that use ROS. We find that the inconsistency type ‘Assigning multiple units to a variable’ accounts for 75% of inconsistencies in ROS code. We identify the ROS classes and physical units most likely to be involved with dimensional inconsistencies, and find that the ROS Message type geometry_msgs::Twist is involved in over half of all inconsistencies and is used by developers in ways contrary to Twist's intent. We further analyze the frequency of physical units used in ROS programs as a proxy for assessing how developers use ROS, and discuss the practical implications of our results including how to detect and avoid these inconsistencies.

Dimensional inconsistencies in code and ROS messages: A study of 5.9M lines of code

Modern robotics systems rely on distributed event-based frameworks to facilitate the assembly of software out of collections of reusable components. These frameworks express component dependencies in data that encode event publish-subscribe relations. This loosely coupled architecture makes it difficult for developers to understand the dependencies and to predict the impacts of a change to a component as the components grow in number and complexity. Moreover, this encoding of dependencies renders traditional techniques for analyzing component dependencies inapplicable, because the dependencies are bound by communication channels rather than data. In this work, we present a program analysis technique that automatically extracts a model of component dependencies from distributed system source code. This model identifies not only the temporal dependencies among components, but also the conditions under which those dependencies are realized. We have implemented the analysis and applied it to systems developed in ROS. The resulting models are succinct and precise, which suggests that programmers will find them comprehensible, and they can be used to document important global dependencies in a system, to compare different versions to identify the impacts of component changes, and to help locate errors.

Extracting conditional component dependence for distributed robotic systems

The significant impact of TCP congestion control on the Internet highlights the importance of testing the correctness and performance of congestion control algorithm implementations (CCAIs) in various network environments. Many CCAI testing questions can be answered by exploring the numerical state space of CCAIs, which is defined by a group of numerical (and nonnumerical) state variables of the CCAIs. However, the current practices for automated numerical state space exploration are either limited by the approximate abstract CCAI models or inefficient due to the large space of network environment parameters and the complicated relation between the CCAI states and network environment parameters. In this paper, we propose an automated numerical state space exploration method, called ACT, which leverages the model-agnostic feature of random testing and greatly improves its efficiency by guiding random testing under the feedback iteratively obtained in a test. Our experiments on five representative Linux TCP CCAIs show that ACT can more efficiently explore a large numerical state space than manual testing, undirected random testing, and symbolic execution based testing, while without requiring an abstract CCAI model. ACT successfully detects multiple design and implementation bugs of these Linux TCP CCAIs, including some new bugs not reported before.

/pdf/model-agnostic-and-efficient-exploration-of-numerical-state-2bjagujr3f.pdf

Sebastian Elbaum

Papers

Empirically driven SE research: state of the art and required maturity

A methodology for operational profile refinement

Dimensional inconsistencies in code and ROS messages: A study of 5.9M lines of code

Extracting conditional component dependence for distributed robotic systems

Model-Agnostic and Efficient Exploration of Numerical State Space of Real-World {TCP} Congestion Control Implementations