Debian 套件打包教學指南 Lucas Nussbaum packaging-tutorial@packages.debian.org version 0.29 – 2021-11-03 Debian 套件打包教學指南 1 / 90 關於此教學指南 ▶ 目標: 瞭解 Debian 套件打包的相關知識 ▶ 修改既有套件 ▶ 新增自有套件 ▶ 和 Debian 社群進行交流 ▶ 成為 成為 Debian 進階使用者 ▶ 這份教學指南針對重要功能進行介紹, 但也許會有疏漏之處 ▶ 所以你需要閱讀更多文件 ▶ 文件大部份的內容也適用於 Debian 衍生的 Linux發行版 ▶ 其中包含 Ubuntu Debian 套件打包教學指南 2 / 90 大綱 1 介紹 2 製作原始碼套件 3 構建並測試套件 4 實際演練 1: 修改 grep 套件 5 進階打包主題 進階打包主題 6 維護 Debian 套件 7 結論 8 深入淺出實際演練 9 深入淺出實際演練 Debian 套件打包教學指南 3 / 90 大綱 1 介紹 2 製作原始碼套件 3 構建並測試套件 4 實際演練 1: 修改 grep 套件 5 進階打包主題 6 維護 Debian 套件 7 結論 8 深入淺出實際演練 9 深入淺出實際演練 Debian 套件打包教學指南 4 /

Debian 新維護人員手冊 Josip Rodin, Osamu Aoki, Aron Xu, 李凌, 郑原真, 陳侃如, 青木修, 且周默 Debian 新維護人員手冊 ii 版權 © 1998-2002 Josip Rodin 版權 © 2005-2015 Osamu Aoki 版權 © 2010 Craig Small 版權 © 2010 Raphaël Hertzog 本文件可在 通用公共許可證第二版或更高版本的條款規定下使用。 本文檔在撰寫過程中參考了以下兩篇文檔： • Making a Debian Package (AKA the Debmake Manual), copyright © 1997 Jaldhar Vyas. • The New-Maintainer’s Debian Packaging Howto, copyright © 1997 Will Lowe. The examples is available as ”Guide for Debian Maintainers”. Please use this new tutorial as the primary tutorial document. Debian 新維護人員手冊 iii COLLABORATORS TITLE : Debian 新維護人員手冊 ACTION NAME DATE SIGNATURE

rust-analyzer 通訊，後者提供適用於 VS Code、Emacs、Vim/Neovim 等的自動完成和跳至定義功能。此外，您也可以 使用稱做 RustRover 的不同 IDE。 • On Debian/Ubuntu, you can also install Cargo, the Rust source and the Rust formatter via apt. However, this i8、i16、i32、i64、i128、isize -10、0、1_000、123_i64 非帶號整數 u8、u16、u32、u64、u128、usize 0、123、10_u16 浮點數 f32、f64 3.14、-10.0e20、2_f32 萬國碼純量值 char 'a'、' α '、' ∞' 布林值 bool true、false 型別的寬度如下： • iN、uN 和 fN 的寬度為 N 位元 • isize 的結尾不是字元邊界，因此 程式會發生恐慌。請根據錯誤訊息，將其調整至結尾為字元邊界的範圍。 26 • 原形字串可讓您建立停用逸出功能的 &str 值：r"\n" == "\\n"。只要在引號兩側使用等量的 #，即可嵌入雙引號： fn main() { println!(r#"link"#); println!("link");

. . . . . . . . . . . . . . . 455 36.21 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 455 36.22 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 478 38.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 38.3 Noteworthy differences from Python DOCUMENTATION 3 Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow

. . . . . . . . . . . . . . . 455 36.21 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 455 36.22 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 478 38.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 38.3 Noteworthy differences from Python DOCUMENTATION 3 Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow

. . . . . . . . . . . . . . . 489 37.22 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 489 37.23 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 513 39.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 39.3 Noteworthy differences from Python languages. Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow

. . . . . . . . . . . . . . . 489 37.22 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 489 37.23 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 513 39.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 39.3 Noteworthy differences from Python languages. Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow

. . . . . . . . . . . . . . . 489 37.22 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 489 37.23 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 513 39.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 39.3 Noteworthy differences from Python languages. Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow

. . . . . . . . . . . . . . . 500 37.23 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 500 37.24 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 524 39.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 526 39.3 Noteworthy differences from Python languages. Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow

. . . . . . . . . . . . . . . 501 37.22 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 501 37.23 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 525 39.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 39.3 Noteworthy differences from Python languages. Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow