Lab 1: Dependency Analysis and Dependency Graph¶

小组成员

201932110105沈音棋

201932110104马奕琪

201932110106孙仪杰

201932110107田西芷

项目GitHub地址: EnglishPal.

项目Read the Doc地址: Read the Doc.

Abstract¶

找出EnglishPal中模块/类/函数之间的依赖关系，绘制依赖图，分析EnglishPal当前依赖关系利弊。

Introduction¶

EnglishPal是智能捕捉阅读弱点，针对性提高用户的阅读水平的网站。提高用户的获取英文信息的速度与准确度。提供精选短文和生词簿，对于英语老师，English Pal 可以帮助掌握题目词汇规律，提高教学质量。对于英语学着，English Pal可以帮助迅速提高词汇，轻松应对各种考试。本次实验针对EnglishPal源代码，分析当前模块级依赖关系以及类/函数级依赖关系，分析当前体系结构。

Methods and materials¶

①Snakefood：从Python代码中生成依赖，过滤，聚类，并从依赖列表中生成图表。使用Snakefood捕获模块级依赖关系。

②Graphviz：开源的图形可视化软件。使用graphviz online渲染依赖关系图。

③Mermaid：基于Javascript的图表和图表工具，使用文本和代码创建图表和可视化，以便动态地创建和修改图表。使用mermaid live editor渲染类/函数级依赖关系图。

Results¶

snakefood.dot

# This file was generated by snakefood3.

strict digraph "dependencies" {
    graph [
        rankdir = "LR",
        overlap = "scale",
        size = "8,10",
        ratio = "fill",
        fontsize = "16",
        fontname = "Helvetica",
        clusterrank = "local"
        ]

       node [
           fontsize=12
           shape=box
       ];

"difficulty.py"  [style=filled];
"difficulty.py" -> "wordfreqCMD.py";
"difficulty.py" -> "math";
"difficulty.py" -> "pickle.py";
"main.py"  [style=filled];
"main.py" -> "WordFreq.py";
"main.py" -> "wordfreqCMD.py";
"main.py" -> "UseSqlite.py";
"main.py" -> "pickle_idea.py";
"main.py" -> "pickle_idea2.py";
"main.py" -> "os.py";
"main.py" -> "random.py";
"main.py" -> "glob.py";
"main.py" -> "datetime.py";
"main.py" -> "flask.py";
"main.py" -> "difficulty.py";
"pickle_idea.py"  [style=filled];
"pickle_idea.py" -> "pickle.py";
"pickle_idea.py" -> "datetime.py";
"pickle_idea2.py"  [style=filled];
"pickle_idea2.py" -> "pickle.py";
"pickle_idea2.py" -> "datetime.py";
"UseSqlite.py"  [style=filled];
"UseSqlite.py" -> "sqlite3";
"WordFreq.py"  [style=filled];
"WordFreq.py" -> "wordfreqCMD.py";
"WordFreq.py" -> "string.py";
"wordfreqCMD.py"  [style=filled];
"wordfreqCMD.py" -> "collections";
"wordfreqCMD.py" -> "string.py";
"wordfreqCMD.py" -> "operator.py";
"wordfreqCMD.py" -> "os.py";
"wordfreqCMD.py" -> "sys";
"wordfreqCMD.py" -> "pickle_idea.py";

}

依赖图

mermaid.txt

classDiagram
    WordFreq ..> wordfreqCMD
    wordfreqCMD <.. difficulty
    wordfreqCMD..> pickle_idea
    main ..> wordfreqCMD
    main ..> WordFreq
    pickle_idea ..> pickle
    pickle_idea2 ..> pickle
    Sqlite3Template <|-- InsertQuery
    Sqlite3Template <|-- RecordQuery
    main ..> InsertQuery
    main ..> RecordQuery

    class difficulty{
    +load_record(pickle_fname)
    +difficulty_level_from_frequency(word, d)
    +get_difficulty_level(d1, d2)
    +revert_dict(d)
    +user_difficulty_level(d_user, d)
    +text_difficulty_level(s, d)
}
    class pickle_idea{
    +lst2dict(lst, d)
    +dict2lst(d)
    +merge_frequency(lst1, lst2)
    +load_record(pickle_fname)
    +save_frequency_to_pickle(d, pickle_fname)
    +unfamiliar(path,word)
    +familiar(path,word)
}
    class pickle_idea2{
    +lst2dict(lst, d)
    +deleteRecord(path,word)
    +dict2lst(d)
    +merge_frequency(lst1, lst2)
    +load_record(pickle_fname)
    +save_frequency_to_pickle(d, pickle_fname)
}
    class pickle{

}
    class Sqlite3Template{
    +__init__(self, db_fname)
    +connect(self, db_fname)
    +instructions(self, query_statement)
    +operate(self)
    +format_results(self)
    +do(self)
    +instructions_with_parameters(self, query_statement, parameters)
    +do_with_parameters(self)
    +operate_with_parameters(self)
}
    class InsertQuery{
    +instructions(self, query)
}
    class RecordQuery{
    +instructions(self, query)
    +format_results(self)
    +get_results(self)
}
    class WordFreq{
    +__init__(self, s)
    +get_freq(self)
}
    class wordfreqCMD{
    +freq(fruit)
    +youdao_link(s)
    +file2str(fname)
    +remove_punctuation(s)
    +sort_in_descending_order(lst)
    +sort_in_ascending_order(lst)
    +make_html_page(lst, fname)
}
    class main{
    +get_random_image(path)
    +get_random_ads()
    +total_number_of_essays()
    +load_freq_history(path)
    +verify_user(username, password)
    +add_user(username, password)
    +check_username_availability(username)
    +get_expiry_date(username)
    +within_range(x, y, r)
    +get_article_title(s)
    +get_article_body(s)
    +get_today_article(user_word_list, articleID)
    +appears_in_test(word, d)
    +get_time()
    +get_question_part(s)
    +get_answer_part(s)
    +get_flashed_messages_if_any()
    +highlight(text, word)
    +user_reset(username)
    +mark_word()
    +mainpage()
    +user_mark_word(username)
    +unfamiliar(username,word)
    +familiar(username,word)
    +deleteword(username,word)
    +userpage(username)
    +signup()
    +login()
    +logout()
}

依赖图

依赖分析：¶

①wordfreqCMD.py

wordfreqCMD将字符串转换为列表，得到每个单词的词频；wordFreq依赖于wordfreqCMD，初始化文章；difficulty依赖于wordfreqCMD，计算文章的难度等级；main依赖于wordfreqCMD，有道的链接和按降序排列。

②pickle

pickle_idea依赖于pickle，列表llist中的信息存储到字典中，记录单词和词频；pickle_idea2依赖于pickle，列表llist中的信息存储到字典中，记录单词和日期；

将这些函数合并到wordfreqCMD.py中，wordfreqCMD依赖于pickle_idea，显示积累频率。

③UseSqlite.py

Sqlite3Template 连接数据库；InsertQuery继承Sqlite3Template，插入数据；RecordQuery继承Sqlite3Template，记录数据。

④Main.py

获取文章get_today_article，依赖于RecordQuery数据记录、调用方法get_difficulty_level根据读者的阅读水平选择文章、根据pickle_idea2根据记录高亮生词；显示主页面mainpage，依赖于WordFreq、wordfreqCMD；问题与答案交互方法get_question_part、get_answer_part等；用户页面，勾选生词userpage，依赖于WordFreq、wordfreqCMD；单词熟悉度操作，包括load_freq_history；unfamiliar；familiar等；用户注册登录退出signup、login、logout、add_user依赖于InsertQuery。

架构利弊分析¶

弊端：EnglishPal的主要页面与功能集中在main.py中，功能代码中嵌入了大量网页前端代码，前后端混杂。进行前后端分离的重构有助于解放前端，不需要向后台提供模板，或是在前端html中嵌入后台代码；提高工作效率，分工更加明确；局部性能提升，实现页面的按需加载，在页面交互和用户体验上有所提升；降低维护成本，快速定位问题所在。

优点：前后端之间没有通信成本，功能清晰，无大量重复代码。使用适用于小型网站的Flask框架，使得项目灵活，可扩展性强，第三方库的选择多。

Discussion

通过对EnglishPal的依赖关系分析，对EnglishPal的架构有了清晰的认知。学习使用snakefood生成模块级依赖关系snakefood.dot，并用graphviz渲染；用mermain绘制类/函数级依赖关系图。掌握对已有项目的结构和依赖关系分析方法，对项目重构有更深的认识。