Thinking Computationally

Solving problems is a central part of being a programmer, as well as a useful skill for everyday life. The methodology is broadly the same wherever you look: identify smaller, more tractable challenges; realize these as instances of a general class of problem; solve the intermediate challenges; and put everything together as a sequence of simple steps to solve the larger problem. In computer science, we call this computational thinking.

This chapter serves as an introduction to the basic components of computational thinking at a high level. The objectives are to lay the foundation for more detailed analysis and in-depth examples later in the book. The first part of the chapter introduces the four components of computational thinking (decomposition, abstraction, pattern recognition, and algorithm design). The second half of the chapter deals with C++ specifically and identifies some aspects of the C++ language and standard library that can not only help implement efficient solutions, but also help you think about the problems themselves.

It is important to remember that the four components of computational thinking are not a step-by-step guide to solving problems. Learning how and when these different components come together to deliver a solution relies on a good knowledge of the tools and methodologies available to you as the solver, and on your past experience. This chapter will help you get started with building the necessary foundations of the theory and set the stage for building out some basic examples to get you started with tackling larger and more complex problems later.

Solving problems is an iterative process. There will be many failed attempts and false starts. This is a necessary part of the process. The last part of the chapter deals with good software practices that will enable you to iterate quickly and easily on your designs and arrive at a correct and usable solution more quickly.

In this chapter, we’re going to cover the following main topics:

• The components of computational thinking
• Decomposing problems
• Building abstractions and recognizing common patterns
• Understanding algorithms
• Using modern C++ and good practice