This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

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

We describe Veins, an open-source model library for (and a toolbox around) OMNeT++, which supports researchers conducting simulations involving communicating road vehicles—either as the main focus of a study or as a component. Veins already includes a full stack of simulation models for investigating cars and infrastructure communicating via IEEE 802.11 based technologies in simulations of Vehicular Ad Hoc Networks (VANETs) and Intelligent Transportation Systems (ITS). Thanks to its modularity, though, it can equally well be used as the basis for modeling other mobile nodes (like bikes or pedestrians) and communication technologies (from mobile broadband to visible light). Serving as the basis for hundreds of publications and university courses since its beginnings in the year 2006, today Veins is both one of the most mature and established tools in this domain.

Veins: The Open Source Vehicular Network Simulation Framework

Test suites should test exceptional behavior to detect faults in error-handling code. However, manually-written test suites tend to neglect exceptional behavior. Automatically-generated test suites, on the other hand, lack test oracles that verify whether runtime exceptions are the expected behavior of the code under test. This paper proposes a technique that automatically creates test oracles for exceptional behaviors from Javadoc comments. The technique uses a combination of natural language processing and run-time instrumentation. Our implementation, Toradocu, can be combined with a test input generation tool. Our experimental evaluation shows that Toradocu improves the fault-finding effectiveness of EvoSuite and Randoop test suites by 8% and 16% respectively, and reduces EvoSuite’s false positives by 33%.

Automatic generation of oracles for exceptional behaviors

Most of today's Advanced Driver Assistance Systems (ADASs) rely on the data of local perception sensors. With the introduction of Vehicle-2-X (V2X) communication, the perception range of next generation vehicles will be extended even further. As a technology relying on the presence of other communication partners (network effect), any measure of increasing the market penetration rate of the technology should be pursued. This paper introduces the concept of collective perception , which aims at publishing the objects perceived by local perception sensors in addition to the envisioned self-announcement procedure of V2X enabled vehicles. The potential of the concept is studied by employing an holistic Vehicular Ad-hoc Network (VANET) simulation with a modular extension for local perception sensors. In the simulation scenarios, V2X enabled vehicles publish themselves along with their locally perceived objects. The findings unveil a significant leverage of the technology, especially in the case of low market penetration rates.

The potential of collective perception in vehicular ad-hoc networks

Development and testing of novel Advanced Driver Assistance Systems (ADASs) based on Vehicle-2-X (V2X) communication is often supported by simulations. However, simulation models for Vehicular Ad Hoc Networks (VANETs) can easily become very complex due to the diverse set of involved components such as vehicle mobility, radio propagation, network protocols and application behavior. Therefore, we present an extension to the V2X simulation framework “Vehicles in Network Simulation” (Veins) with a clear separation of concerns regarding network and application aspects. Because of distinctively modeled Facilities and Application layers, this approach enables the concurrent combination of multiple VANET application sets as well as the evaluation of their interdependencies. The proposed extension to Veins, named Artery, incorporates an implementation of the ETSI ITS-G5 protocol stack following the European specifications for V2X communication. State and perception data required for ADAS algorithms are collected and provided in an extensible manner for each vehicle. Thus, Artery represents a more holistic approach in testing novel VANET applications in a network simulation environment.

Artery: Extending Veins for VANET applications

In conjunction with automated vehicles, Inter-Vehicle Communication represents the next ‘big thing’ towards the vision of cooperative driving, where road participants share information about their planned behaviour and where hazardous situations are solved or avoided jointly. Several requirements need to be fulfilled to reach this long-term goal. One requirement is a common information base, where all road participants are fully aware of each other. The idea of collective perception contributes to this common information base by sharing local sensor data with other road participants. Whereas most related work focuses on the aspects of sensor data fusion, we focus on the implications of an ETSI ITS G5 based network for collective perception. We present and analyse different message formats and dissemination variants for sharing sensor data. Their usability is validated in an extensive microscopic simulation study. In particular, implications caused by Decentralized Congestion Control as proposed by standardisation are assessed in a constrained environment with hundreds of vehicles.

Collective perception and decentralized congestion control in vehicular ad-hoc networks

In the domain of automated driving, numerous (technological) problems were solved in recent years, but still many limitations are around that could eventually prevent the deployment of automated driving systems (ADS) beyond SAE level 3. A remote operating fallback authority might be a promising solution. In order for teleoperation to function reliably and universal, it will make use of existing infrastructure, such as cellular networks. Unfortunately, cellular networks might suffer from variable performance. In this work, we investigate the effects of latency on task performance and perceived workload for different driving scenarios. Results from a simulator study (N=28) suggest that latency has negative influence on driving performance and subjective factors and led to a decreased confidence in Teleoperated Driving during the study. A latency of about 300 ms already led to a deteriorated driving performance, whereas variable latency did not consequently deteriorate driving performance.

Teleoperation: The Holy Grail to Solve Problems of Automated Driving? Sure, but Latency Matters

Teleoperated Driving is the remote control driving of a vehicle by a human driver. The concept of Teleoperated Driving requires the use of mobile networks, which typically experience variable throughput, variable latency and uneven network coverage. To investigate whether Teleoperated Driving can be possible with contemporary mobile networks, we have conducted measurements while driving with vehicles in the real world. We used complementary measurement setups to obtain results that can be compared. The dataset consists of about 5200 km (4660 minutes) driving measurements. Results show that Teleoperated Driving could be possible, but the high variance of network parameters makes it difficult to use the system at all times. It appears that the speed of the vehicle and the distance to the base station may not influence Teleoperated Driving, while handover with changed radio technology, signal strength and distance to the teleoperation station may have an impact. Possible mitigations to overcome these problems along with a basic whitelisting approach is discussed.

/pdf/measuring-the-feasibility-of-teleoperated-driving-in-mobile-5fzmmy0xe7.pdf

Christian Facchi

Papers

Artery: Extending Veins for VANET applications

Collective perception and decentralized congestion control in vehicular ad-hoc networks

Teleoperation: The Holy Grail to Solve Problems of Automated Driving? Sure, but Latency Matters

Measuring the Feasibility of Teleoperated Driving in Mobile Networks

The Effect of Decentralized Congestion Control on Collective Perception in Dense Traffic Scenarios