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Understanding Software

Understanding Software

By : Max Kanat-Alexander
3.8 (11)
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Understanding Software

Understanding Software

3.8 (11)
By: Max Kanat-Alexander

Overview of this book

In Understanding Software, Max Kanat-Alexander, Technical Lead for Code Health at Google, shows you how to bring simplicity back to computer programming. Max explains to you why programmers suck, and how to suck less as a programmer. There’s just too much complex stuff in the world. Complex stuff can’t be used, and it breaks too easily. Complexity is stupid. Simplicity is smart. Understanding Software covers many areas of programming, from how to write simple code to profound insights into programming, and then how to suck less at what you do! You'll discover the problems with software complexity, the root of its causes, and how to use simplicity to create great software. You'll examine debugging like you've never done before, and how to get a handle on being happy while working in teams. Max brings a selection of carefully crafted essays, thoughts, and advice about working and succeeding in the software industry, from his legendary blog Code Simplicity. Max has crafted forty-three essays which have the power to help you avoid complexity and embrace simplicity, so you can be a happier and more successful developer. Max's technical knowledge, insight, and kindness, has earned him code guru status, and his ideas will inspire you and help refresh your approach to the challenges of being a developer.
Table of Contents (15 chapters)
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1
Table of Contents
2
Understanding Software
3
Credits
4
About the Author
6
Customer Feedback
7
Foreword
15
Index

Chapter 10. The Accuracy of Future Predictions

One thing we know about software design is that the future is important. However, we also know that the future is very hard to predict.

I think that I have come up with a theory to explain exactly how hard it is to predict the future of software.

The most basic version of this theory is:

The accuracy of future predictions decreases relative to the complexity of the system and the distance into the future you are trying to predict.

As your system becomes more and more complex, you can predict smaller and smaller pieces of the future with any accuracy. As it becomes simpler, you can predict further and further into the future with accuracy.

For example, it's fairly easy to predict the behavior of a "Hello, World" program quite far into the future. It will, most likely, continue to print "Hello, World" when you run it. Remember that this is a sliding scale – sort of a probability of how much you can say about...

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