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09 Sep 09

97 Things Every Programmer Should Know

Welcome to the home page for the 97 Things Every Programmer Should Know project, pearls of wisdom for programmers collected from leading practitioners.

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programming concept

01 Jun 09

THE DAO OF PROGRAMMING Geoffrey James

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concept programming essay

22 May 09

Flesh and Machines 我們都是機器人

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3star AFSM AI behavior-based book choice concept iRobot robot subsumption

追求神乎其技的程式設計之道(七） | vgod’s blog

上一篇談到了優秀程式設計師的第一要件：「熱情」，這一篇我想要談我覺得熱情之外最重要的能力：「思考」，特別是抽象化的思考能力

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concept programming

所有的程式都可以看成一個巨大的金字塔，頂端是這個程式的最終目標，一個模糊的概念；底部是細節的程式碼。而中間是一個經由不斷切割與抽象化所構成的高塔，每一個程式都是切割為許多的元件、模組，再切為更細的class和function，再來是最底下的變數與邏輯判斷式
平凡和偉大的程式設計師，我覺得他們之間的差別就在於能看到多少這個高塔中間的分層。厲害的高手都很善於切換自己思考的高度，一下能跟你討論高階的系統架構設計，一下又能深入到最底下的組合語言和二進位除錯。他們腦中除了有這高塔每一層的詳盡平面圖，甚至也非常了解不同樓層之間的交互關係
1 more annotations...
- 程式語言是很重要的工具，它不只是讓人用不同語法和電腦溝通，而是讓人能用完全不同的思考方式來解決問題。簡單的說，我覺得程式語言就是決定思考高度的一個關鍵因素，而這也間接決定了寫程式的能力

追求神乎其技的程式設計之道(六） | vgod’s blog

blog.vgod.tw/...divine-code-6 - Preview

concept programming

程式員的兩種型態，一是重視演算法、資料結構、執行效率的「效率魔人」，二是重視程式架構、擴充性、彈性、可理解性的「架構狂」。這兩種人其實都很好，要完成一個偉大的軟體，團隊中兩種人一定都要有。比較糟糕的是，有很多「第三型態人」，他們的信念只有一條：「程式只要會動就好」。第三型態人不在乎效率，也不管架構漂不漂亮，上面要求他做什麼，他就想辦法東湊西湊，從Google找程式剪貼，從MSDN抓範例來用，反正只要能隨便測過一個case就能交差了
要成為一個優秀的程式設計師的關鍵是什麼？關鍵不在於coding速度有多快、懂多少演算法，或是背了多少patterns，最重要的是「熱情」
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- 偉大的程式設計師都非常喜歡寫程式，寫程式的過程是一種絕妙的享受，他們執著的地方或許不同，可能是程式的效率，也可能是開發的效率，甚至是架構的彈性或是程式碼的精簡美觀程度，但他們都非常想要並堅持自己應該寫出「好程式」。熱情能驅動他們把軟體的某一個面向雕琢到極致，這需要超乎常人的毅力和堅持，以及絕不向壓力妥協的精神。只要具備這種熱情，不管你在乎的是什麼，都可以成為一名偉大的程式設計大師

11 May 08

Rob Pike: Notes on Programming in C

What follows is a set of short essays that collectively encourage a philosophy of clarity in programming rather than giving hard rules.

www.lysator.liu.se/...pikestyle.html - Preview

C programming concept

Length is not a virtue in a name;
clarity of expression is.
When the variable names are
huge, it's harder to see what's going on.
13 more annotations...
- As in all other aspects of readable programming,
  consistency is important in naming.
- I prefer minimumlength but
  maximuminformation names, and then let the context fill in the
  rest. Globals, for instance, typically have little context
  when they are used, so their names need to be relatively
  evocative. Thus I say maxphysaddr (notMaximumPhysicalAddress) for a global variable, but np
  not NodePointer for a pointer locally defined and used
- Stepping
  through structures using pointers can be much easier to read
  than with expressions: less ink is needed and less effort is
  expended by the compiler and computer.
- if you find code containing many similar,
  complex expressions that evaluate to elements of a data
  structure, judicious use of pointers can clear things up.
- Procedure names should reflect what they do; function
  names should reflect what they return.
- If your code needs a comment to be
  understood, it would be better to rewrite it so it's easier
  to understand.
- Most programs are too complicated - that is, more
  complex than they need to be to solve their problems
  efficiently. Why? Mostly it's because of bad design
- Fancy algorithms are slow when n is small, and
  n is usually small.
- Use simple algorithms
  as well as simple data structures.
- Data dominates. If you've chosen the right
  data structures and organized things well, the algorithms
  will almost always be selfevident. Data structures, not
  algorithms, are central to programming.
- Perhaps the most intriguing aspect of this kind of
  design is that the tables can sometimes be generated by
  another program - a parser generator, in the classical case.
  As a more earthy example, if an operating system is driven
  by a set of tables that connect I/O requests to the
  appropriate device drivers, the system may be `configured'
  by a program that reads a description of the particular
  devices connected to the machine in question and prints the
  corresponding tables.
- Combining datadriven programs with function pointers
  leads to an astonishingly expressive way of working, a way
  that, in my experience, has often led to pleasant surprises.
  Even without a special OO language, you can get 90% of the
  benefit for no extra work and be more in control of the
  result.
- Simple rule: include files should never include include
  files. If instead they state (in comments or implicitly)
  what files they need to have included first, the problem of
  deciding which files to include is pushed to the user
  (programmer) but in a way that's easy to handle and that, by
  construction, avoids multiple inclusions.

10 May 08

Harvard Mathematics Department : Computing: programming

Several chapters of "The Practice of Programming by B.W. Kernighan and R. Pike" contain rules or guidelines that summarize a discussion and are listetd in the Appendix of that book.

www.math.harvard.edu/...index.html - Preview

programming concept

Use descriptive names for globals, short names for locals.
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- Avoid function macros.
- Don't belabor the obvious.
- Comment functions and global data.
  Don't comment bad code, rewrite it.
- Interfaces
  
  Hide implementation details.
  Choose a small orthogonal set of primitives.
  Don't reach behind the user's back.
  Do the same thing the same way everywhere.
  Free a resource in the same layer that allocated it.
  Detect errors at a low level, handle them at a high level.
  Use exceptions only for exceptional situations.
- Debugging
  
  Look for familiar patterns.
  Examine the most recent change.
  Don't make the same mistake twice.
  Debug it now, not later.
  Get a stack trace.
  Read before typing.
  Explain your code to someone else.
  Make the bug reproducible.
  Divide and conquer.
  Study the numerology of failures.
  Display output to localize your search.
  Write self-checking code.
  Write a log file.
  Draw a picture.
  Use tools.
  Keep records.
- Use only features available everywhere.
  Avoid conditional compilation.
  Localize system dependencies in separate files.
- Use a fixed byte order for data exchange.

05 Mar 08

Thinker: 望文生義

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Python concept programming

25 Sep 07

Refactoring: Improving the Design of Existing Code

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book choice concept programming

30 Jun 07

Strong Typing vs. Strong Testing

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Java Python UnitTest concept essay programming type

Most
Python tests happen at runtime rather than at compile time, but they do happen,
and that's the important thing (not when).
If it's not tested, it's broken.
3 more annotations...
- without a full set of unit tests (at the very least), you can't guarantee
  the correctness of a program.
- Strong testing, not strong typing.
- if a Python program has adequate unit tests, it can be as robust as a C++,
  Java or C# program with adequate unit tests

30 May 07

信息流在智能系统中的角色

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actuator concept research robot sensor

智能不仅仅局限于大脑。在整个控制过程中，大脑，执行器以及传感器都对产生我们认为是智能的行为有着非常重要的意义。这就意味着，在定义机器人的行为时，你机器人的尺寸也要被考虑进去

26 May 07

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York Jong's Library tagged concept → View Popular

97 Things Every Programmer Should Know

THE DAO OF PROGRAMMING Geoffrey James

Flesh and Machines 我們都是機器人

追求神乎其技的程式設計之道(七） | vgod’s blog

追求神乎其技的程式設計之道(六） | vgod’s blog

Rob Pike: Notes on Programming in C

Harvard Mathematics Department : Computing: programming

Testing

Interfaces

Debugging

Thinker: 望文生義

Refactoring: Improving the Design of Existing Code

Strong Typing vs. Strong Testing

信息流在智能系统中的角色

鼯鼠與軟體專案開發

高手的秘訣<<RoboticFan（機器人愛好者）

抽象滲漏法則

Thinker: 軟體元件的使用

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