It is close to 1000 pages. Various works by Kent Beck Various works by Barry Boehm Dijkstra, Edsger. The second half of the book is pretty much a catalog of refactoring techniques. It collects a ton of best practices and advices. Make code readable first, and only optimize when you can make measurable improvements to measurable performance bottlenecks. It does not matter exactly what the process is; in fact, the process should be tailored to the problem at hand.
Computers do not care about programs being readable, but people do, and people are going to read your programs many time. It goes into very tiny details of construction process such as steps in building new classes, pros and cons of using data and control structures, debugging, code tuning ideas, refactoring, and many more. Construction is also an area that, traditionally, has not has as much attention to it as other areas such as high level design or testing. I feel like I should have read it earlier in my carrier. Although I don't agree with everything in the book and a few parts feel out of date, it provides an excellent framework for how to think about programming and software engineering. So that you can revisit your notes from time to time in order not to forget most of the ideas.
Having a discussion forum like a book club was an excellent way to solidify my understanding of things I thought I already knew, and to break down some misunderstandings with opinions and feedback from the group. High quality classes and routines provide consistent abstractions, document their assumptions, and check their invariants defensively; they fail sooner rather than later. If I'd read it a decade or so ago it would have saved me a lot of unnecessarily painful learning experiences and teachable moments. No matter what your experience level, programming education, or nature and size of project, this book can give you insights into effective programming practices, stimulating your thinking ability to help you develop the highest quality code. I'm no Agile Zealot, but I do like to be lean and nimble. Steve gives some amazing stats that are often quite surprising and inspiring, and are usually well backed although, I'm guessing he could've found stats to back any statement he wanted to make.
. One key point of this section is that the goals of a certain construction project should be clear. High quality code manages complexity. This section also points out that code reviews, testing, debugging, refactoring, and code tuning all have the potential to improve code quality, but it is when they are used thoughtfully in unison that their potential is maximized. I bought the first edition, read about 400-500 pages and then the book was lost in a move. I think the book is best approached especially for more experienced devs with a 'back to basics' mindset or an 'I'm starting fresh here. Developers should be wary of absolutes and try to avoid blind faith in the processes they use.
Code Complete Low-Level Implementation Structures Level 3: Jackson's Rules of Optimization: One example McConnell gives writing an assertion library if your language does not support assertions. Sure it can be dated at some places and not always applicable to small companies development capabilities but it definitely conveys ideas that all developers should understand and embrace. Paul Oman and Curtis Cook. As project size increases, project quality and development speed tend to go down in a faster than linear manner. I recommend that book for Mid-Senior developers.
After spending some time in the industry and facing with harsh realities of software development, I decided to give it a try again. Many ideas are very controversial, and to yet unformed minds they may be really wrecking. Software has such a knack at becoming complex, and without always striving for simplicity, the complexity will overwhelm anyone. The book is filled with nuggets of wisdom. Those parts of the high level pseudocode that the developer decides to leave in provide automatic, high level commenting of code. One example McConnell gives writing an assertion library if your language does not support assertions.
This edition of the book is out of print, but you might still be able to find it used. If you like my books, you might also be interested in my. The gist of the story is that you have to measure. Requirements are rarely fixed in stone, bugs are always present, and developers can always find a better way to rewrite code. Note that the plan should be supplemented to the needs and interests of particular developers. These warning signs do not necessarily mean that something is wrong with that part of the program, but they are a good indicator that you should be a little bit suspicious.
Now this classic book has been fully updated and revised with leading-edge practices—and hundreds of new code samples—illustrating the art and science of software construction. Although I don't agree with everything in the book and a few parts feel out of date, it provides an excellent framework for how to think about programming and software engineering. Kinds of Names to Avoid Key Points But the most important take-aways are in Chapter 33, Personal Character: I read through this book with a host of colleagues all with quite different experience levels and in different areas of edirion. I'm no Agile Zealot, but I do like to be lean and nimble. The main goal of Code Complete — 2nd edition, was to shrink the gap between the knowledge and experience of industry gurus and academic professors on the one hand and usual commercial practice on the other. Programming is intellectual work - you must remember it.
Because now I had a chance to reflect on my past experiences while reading. Although with only 5 years of experience I felt like many of the points that Steve brought up were fairly obvious once you've been working as a Software Developer. Struggle with code complexity 2. One of the main lessons taught throughout the book is that code should be easy to understand. It can help programmers of all experience levels to focus on the right things: that code is harder to read than to write, that managing complexity is the main goal of programming, and so on. It is close to pages.
Complexities have tendency to fail. Later you could return to it, to refresh knowledge about that obvious stuff, what we start to ignore with time. The most obvious example of this tendency is that aggressive optimization may make code less readable and prevent beneficial refactorings. Developers almost always assume the bottleneck is in the wrong place odd isn't it? For example — the choice of programming languages has to be made. Even a simple class or routine is worthwhile if it decreases the complexity of reading the code where it is used. His comment piqued my interest.