Book Image

Understanding Software

By : Max Kanat-Alexander
Book Image

Understanding Software

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 (50 chapters)
Understanding Software
About the Author
Customer Feedback
The Engineer Attitude
The Singular Secret of the Rockstar Programmer
Software Design, in Two Sentences
Clues to Complexity
Ways To Create Complexity: Break Your API
When Is Backwards-Compatibility Not Worth It?
Complexity is a Prison
The Accuracy of Future Predictions
Simplicity and Strictness
Two is Too Many
What is a Bug?
What is a Computer?
The Components of Software: Structure, Action, and Results
Software as Knowledge
Simplicity and Security
How We Figured Out What Sucked
Why Programmers Suck
Developer Hubris
"Consistency" Does Not Mean "Uniformity"
Success Comes from Execution, Not Innovation


If nothing about the system or its environment changes, then the result of a test should not change. If a test is passing on my system today but failing tomorrow even though I haven't changed the system, then that test is unreliable. In fact, it is invalid as a test because its "failures" are not really failures – they're an "unknown" result disguised as knowledge. We say that such tests are "flaky" or "non-deterministic."

Some aspects of a system are genuinely non-deterministic. For example, you might generate a random string based on the time of day, and then show that string on a web page. In order to test this reliably, you would need two tests:

  1. A test that uses the random-string generation code over and over to make sure that it properly generates random strings.

  2. A test for the web page that uses a fake random-string generator that always returns the same string, so that the web page test is deterministic.

Of course, you would only need the fake in that second test if verifying...