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GPL-2.0

Zre

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原本是一个正则引擎,用dot可视化状态机,后来增加了很多新东西 可用于检查编译原理作业正确性

基础说明

  1. 下文中凡是涉及到图, 均为使用graphviz生成的gvsvg格式, 可以在src/tools/visualize.py中将他们转化为html文件方便查看. 其中visualize.py是从操作系统:设计与实现 (2022 春季学期) 中某个工具简化而来, 我只保留了 svg图片转html的功能.
  2. 图形化界面都打包在发行版中, 解压缩即可使用.
  3. 没有为正则引擎建立图形化界面, 写的太糟了, 只能实现小部分正则语法.
  4. 语法处理函数有时候要自行加入栈底符号#, 需要注意. 并且尽量只使用#当作栈底. 有的地方是硬编码
  5. 该项目起初在Ubuntu系统上开发, 没有中文输入法, 所有英文注释均为此时所写.
  6. 所有需要自定义保存路径的代码都在main函数中.
  7. ANSI Escape Code在终端上显示颜色的效果非常好, 我很喜欢.
  8. 本文档所有符号都是英文符号, 是为了便于使用markdown语法.
  9. 虽然下文并没有提及, 但我是实现了较好的错误处理机制的, 核心代码在src/tools/zlog.py
  10. 测试文件放在input文件夹中.
  11. 整个项目测试的很少, 有待进一步测试

正则引擎

/src/core/NFA.py /src/core/DFA.py /src/core/zre.py /src/core/graph.py 三个文件包含了该项目最初写的简单的正则引擎 ,graph.py封装了抽象的图,在此基础上依次封装NFADFA在各自文件中,最终在zre.py中实现了汤普森算法和子集构造法,实现从正则语句到NFA到DFA的转化。并生成svg格式的图,我提供了visualize.pysvg图片放到还算好看的html界面中。该算法的实现有很大问题,只能解决很小部分的正则表达式,例如(a|b*)可以生成如下的图.

image-20221026103014594 image-20221026103039535

BNF读取器

/src/BNF.py 包含了将原始BNF语法识别为Token流的抽象 (又是一个小的词法分析 ! ),算是封装的很成功的模块,在后面的所有算法中都用到了,能够读取bnf文件,支持的语法如下

  1. 单层的|,不支持括号嵌套
  2. 每个Token之间用空格分隔,不支持尖括号指定Token,所以不需要加字符串来指定非终结符
  3. 认定::=符号前面的都是非终结符,其他都是终结符,所以不限制非终结符一定要大写
  4. 支持单行使用#注释,不支持行内注释
  5. ε字符视为epsilon,其他均不可

一些符合这个语法的BNF文件示例放在input/下,对于简单的例子,学习编译原理而言是足够使用的

LL(1)分析器

该分析器拥有GUI界面

/src/core/Up_Down.py自顶向下分析算法中的LL(1)识别器,可以依次生成First首符集 Follow随符集 预测分析表,并在此基础上实现递归下降分析,识别一个句子是否符合输入的BNF语法,例如经典的表达式文法input/calc.txt (已经手动消除左递归处理的BNF)

image-20221026104200052

image-20221026104218061

可以成功识别a+a*a这样的句子

image-20221128155624273

算符优先分析器

该分析器拥有GUI界面

/src/core/Operator_First.py 算符优先文法识别器,可以依次生成Firstvt Lastvt算符优先表并可视化,并识别句子,还是以经典的表达式文法为例

image-20221026105227947 image-20221026105239899 image-20221026105311915

并且在识别句子时可以显示整个规约过程, 例如识别i*(i+i)'

image-20221026105422488

算符优先是最小巧我最满意的, 缺点是能识别文法太少了, 这也是算法本身的问题.

SLR(1)分析器

该分析器拥有GUI界面

/src/core/SLR.py实现SLR(1)分析器,是截止当前最复杂的,并且在我的封装下更加复杂了, 首先将产生式分解成带有活前缀的项目,据此生成识别活前缀的DFA,再根据DFA生成action_goto表格,再根据该表格实现SLR(1)分析。整个过程中,生成DFA的过程相比于正则引擎需要汤普森算法需要递归,这里只需要循环迭代即可生成,然而还是在理解上遇到困难,并且现在还是存在一些小问题,例如在生成新的DFA节点时,在某些情况(我也没发现到底是什么情况)下无法合并到已经存在的DFA节点上。接下来以下述BNF语法为例, 该语法不仅是SLR(1)文法, 同时也是LR(0)文法, 事实上本分析器也是支持表达式文法的.

E ::= a A | b B
A ::= c A | d
B ::= c B | d

生成DFA如下. (还记得基础说明中的第一点吗)

image-20221026114629081

生成action_goto表格如下

image-20221026114656610

在分析时可以显示全部的分析过程,例如对于字符串bcd,有如下的分析过程

image-20221026114801996

LR(1)

由于本人开始写LR(0)时还不懂LR(1)算法, 没有为前看符号预留位置, 导致现在要把SLR扩展为LR(1)出现了灾难性的困难 --- 几乎要全部重写. 不想写了

HW

本来这种编译底层的东西做成界面毫无意义, 但是为了学校实验结果看起来炫酷一点, 还是为每个分析器增加了GUI界面, 存放在src/HW目录下. **并且所有可执行文件我都会打包在发行版中. **

其中src/HW/core/zlex.py是为了完成作业单独写的一个词法分析器核心, 对应的界面在src/HW/gui/zlex_gui.py下. 使用方法如下:

  • 输入一个lex文件, 详细可见input/HW/lex0.txt, 这个文件是我调试过的, 使用引号表示匹配相同的字符串, 使用正则表达式匹配数字, 标识符等, 使用冒号分隔Token名和正则文法. 几乎就和Antlr需要的格式一模一样.
  • 读取到lex文件后, 再将源文件喂给程序, 程序就会吐出Token流了.

/HW/gui/zll1_gui.pyLL(1) 算符优先 SLR(1)三个分析器的前端界面.

image-20221128163045623

大逆不道的将其称为Compiler, 实际上只实现了一些语法分析而已. 然而如果以一种广义的角度来说, 这其实也是一种Compiler, 读取lex文件BNF文件, 正则引擎解析, 这些都是一个个小小的编译器. 正是由于这些小小的编译器, 最终才有可能创造出大大的编译器.

总结

  • 我找不到高效的方法回归测试
  • 写着写着自己都忘记前面写的什么了
  • 生成器太顺手了
  • 没有使用qt的资源管理特性, 只把图标等全部存放在同一个位置, 导致项目结构管理会出现一些麻烦的问题.
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词法/语法分析器及其图形化 expand collapse
Python and 3 more languages
GPL-2.0
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