合肥生活安徽新聞合肥交通合肥房產(chǎn)生活服務(wù)合肥教育合肥招聘合肥旅游文化藝術(shù)合肥美食合肥地圖合肥社保合肥醫(yī)院企業(yè)服務(wù)合肥法律

        CS209A代做、Java程序設(shè)計(jì)代寫
        CS209A代做、Java程序設(shè)計(jì)代寫

        時(shí)間:2024-12-25  來源:合肥網(wǎng)hfw.cc  作者:hfw.cc 我要糾錯(cuò)



        Project.md 2024-1**10
        1 / 3
        [CS209A-24Fall] Assignment2 (100 points)
        Background
        In the process of software development, many questions will arise. Developers may resort to Q&A website to
        post questions and seek answers. Stack Overflow is such a Q&A website for programmers, and it belongs to the Stack Exchange Network. Stack
        Overflow serves as a platform for users to ask and answer questions, and, through membership and active
        participation, to vote questions and answers up or down and edit questions and answers in a fashion similar
        to a wiki. Users of Stack Overflow can earn reputation points and "badges"; for example, a person is awarded
        10 reputation points for receiving an "up" vote on a question or an answer to a question, and can receive
        badges for their valued contributions. Users unlock new privileges with an increase in reputation, like the
        ability to vote, comment, and even edit other people's posts. In this final project, we'll use Spring Boot to develop a web application that stores, analyzes, and visualizes
        Stack Overflow Q&A data w.r.t. java programming, with the purpose of understanding the common
        questions, answers, and resolution activities associated with Java programming. Data Collection (10 points)
        On Stack Overflow, questions related to Java programming are typically tagged java. You could use this java
        tag to identify java-related questions. A question and all of its answers and comments are together referred to
        as a thread. For java-related threads on Stack Overflow, we are interested in answering a list of questions as described
        below. You should first collect proper data from Stack Overflow to answer these questions. Please check the
        official Stack Overflow REST API documentation to learn the REST APIs for collecting different types of data. . You may need to create a Stack Overflow account in order to use its full REST API service. · API requests are subject to rate limits. Please carefully design and execute your requests, otherwise
        you may reach your daily quota quickly. . Connections to Stack Overflow REST service maybe unstable sometimes. So, please start the data
        collection ASAP!
        There are over 1 million threads tagged with java on Stack Overflow. You DON'T have to collect them all. Yet, you should collect data for at least 1000 threads in order to get meaningful insights from the data analysis. Important:
        Data collection is offline, meaning that you need to collect and persist the data first. It is recommended that
        you use a database (e.g., PostgreSQL, MySQL, etc.) to store the data. However, it is also fine if you store the
        data in plain files. In other words, when users interact with your application, the server should get the data
        from your local database (or local files), instead of sending REST requests to Stack Overflow on the fly. Hence, the data analysis for the below questions should be performed on the dataset you collected. That is, we first collect a subset of Stack Overflow data (e.g., 1000 threads tagged java) and then answer the
        following questions using this subset.
        Project.md 2024-1**10
        2 / 3
        Part I: Data Analysis (70 points)
        For each question from this part, you should: . Figure out which data is needed to answer the question
        . Design and implement the data analysis on the backend
        . Visualize the results on the frontend using proper charts. In other words, when interacting with your web application from the browser, users could select interested
        analysis, which sends requests to the server; the server performs corresponding data analysis and returns the
        results back to the frontend, which visualizes the results on the webpages. Your work will be evaluated by: . whether the data analysis is meaningful and relevant, i.e., it can indeed answer the question with proper
        they want
        instantly by looking at the visualization. Take a look at the data visualization catalogue for inspirations. 1. Java Topics (10 points)
        We have covered various topics in this course, e.g., generics, collections, I/O, lambda, multithreading, socket, etc. It's interesting to know, what are the top N (N>1, you may choose a proper N depending on your data
        and your UI design, same below) topics that are most frequently asked on Stack Overflow?
        2. User Engagement (15 points)
        What are the top N topics that have the most engagement from users with higher reputation scores? User
        engagement means any user activity (e.g., edit, answer, comment, upvote, downvote, etc.) on the thread. 3. Common Mistakes (15 points)
        Developers make mistakes, which result in bugs in the code. Bugs manifest themselves as errors or
        exceptions, which can be roughly classified as: . Fatal errors: errors like OutOfMemoryError that cannot be recovered at runtime. . Exceptions: checked exceptions and runtime exceptions that can be handled programmatically by
        developers. What are the top N errors and exceptions that are frequently discussed by Java developers?
        Note that, tags are high-level information and may not include low-level errors or exceptions. Hence, for this
        question, you cannot only use tag information. You need to further analyze thread content (e.g., question text
        and answer text) to identify error or exception related information, probably using advanced techniques such
        as regular expression matching. 4. Answer Quality (30 points)
        We consider an answer to be "high-quality" if it is accepted or has many upvotes. It's useful to know, what
        factors contribute to high-quality answers?
        3 / 3
        Project.md 2024-1**10
        Please investigate the following factors: . The elapsed time between question creation and answer creation (e.g., whether the first posted answer
        tends to be accepted?). . The reputation of the user that creates the answer (e.g., whether answers created by high-reputation
        users tend to be accepted or have more upvotes?). In addition to these 2 factors, you should also propose another 1 factor that may contribute to the quality of
        answers. For each of the 3 factors, use proper data analysis and visualizations to demonstrate whether the factor
        contributes to high-quality answers or not. Part II: RESTful Service (20 points)
        Your application should also provide a REST service that answers the following two questions, so that users
        may use RESTful APIs to GET the answers they want. The required REST services include: . Topic frequency: users could query for the frequency of a specific topic. Users could also query for the
        top N topics sorted by frequency. . Bug frequency: users could query for the frequency of a specific error or exception. Users could also
        query for the top N errors or exceptions sorted by frequency. Here, you could reuse the data analysis from Part I. Responses of the REST requests should be in json format. Requirements
        Data Analysis
        You should implement the data analysis by yourself, using Java features such as Collections, Lambda, and
        Stream. You CANNOT feed the data to AI, ask AI to do the analysis, and use AI responses as your data analysis results. You will get 0 point for the question if you do so. Data analysis results should be dynamically generated by the server everytime clients send a request. You
        SHOULD NOT precompute the results and stored it as a static content then simply display the precomputed
        static content on the frontend. 20 points will be deducted if you do so. Web Framework
        You should only use Spring Boot as the web framework. Frontend
        Frontend functionalities, such as data visualization and interactive controls, could be implemented in any
        programming language (e.g., JavaScript, HTML, CSS, etc.) with any 3rd-party libraries or framework.

        請加QQ:99515681  郵箱:99515681@qq.com   WX:codinghelp



         

        掃一掃在手機(jī)打開當(dāng)前頁
      1. 上一篇:代寫DTS207TC、SQL編程語言代做
      2. 下一篇:CS305程序代做、代寫Python程序語言
      3. 無相關(guān)信息
        合肥生活資訊

        合肥圖文信息
        挖掘機(jī)濾芯提升發(fā)動(dòng)機(jī)性能
        挖掘機(jī)濾芯提升發(fā)動(dòng)機(jī)性能
        戴納斯帝壁掛爐全國售后服務(wù)電話24小時(shí)官網(wǎng)400(全國服務(wù)熱線)
        戴納斯帝壁掛爐全國售后服務(wù)電話24小時(shí)官網(wǎng)
        菲斯曼壁掛爐全國統(tǒng)一400售后維修服務(wù)電話24小時(shí)服務(wù)熱線
        菲斯曼壁掛爐全國統(tǒng)一400售后維修服務(wù)電話2
        美的熱水器售后服務(wù)技術(shù)咨詢電話全國24小時(shí)客服熱線
        美的熱水器售后服務(wù)技術(shù)咨詢電話全國24小時(shí)
        海信羅馬假日洗衣機(jī)亮相AWE  復(fù)古美學(xué)與現(xiàn)代科技完美結(jié)合
        海信羅馬假日洗衣機(jī)亮相AWE 復(fù)古美學(xué)與現(xiàn)代
        合肥機(jī)場巴士4號線
        合肥機(jī)場巴士4號線
        合肥機(jī)場巴士3號線
        合肥機(jī)場巴士3號線
        合肥機(jī)場巴士2號線
        合肥機(jī)場巴士2號線
      4. 幣安app官網(wǎng)下載 短信驗(yàn)證碼

        關(guān)于我們 | 打賞支持 | 廣告服務(wù) | 聯(lián)系我們 | 網(wǎng)站地圖 | 免責(zé)聲明 | 幫助中心 | 友情鏈接 |

        Copyright © 2024 hfw.cc Inc. All Rights Reserved. 合肥網(wǎng) 版權(quán)所有
        ICP備06013414號-3 公安備 42010502001045

        主站蜘蛛池模板: 久久精品无码一区二区三区日韩| 日韩精品视频一区二区三区| 污污内射在线观看一区二区少妇 | av无码人妻一区二区三区牛牛 | 亚洲一区二区三区夜色| 多人伦精品一区二区三区视频| 一区二区三区影院| 午夜肉伦伦影院久久精品免费看国产一区二区三区 | 精品无码人妻一区二区三区品 | 久久久久人妻精品一区二区三区| 亚洲视频一区二区三区四区| 国产一区二区三精品久久久无广告| 国产成人欧美一区二区三区| 2018高清国产一区二区三区| 一区二区三区免费在线视频 | 久久精品国产免费一区| 成人精品一区二区激情| 老熟妇仑乱一区二区视頻| 亚洲电影一区二区| 精品一区二区三区在线视频| 亚洲国产精品一区二区第一页免| 国产日韩视频一区| 波多野结衣在线观看一区| 国产在线观看一区二区三区| 无码日韩人妻av一区免费| 一区二区三区在线观看| 亚洲国产成人久久一区二区三区| 天堂Av无码Av一区二区三区| 国产午夜精品一区二区三区极品| 国产小仙女视频一区二区三区| 成人精品一区二区三区电影| 日本一区二区三区精品国产 | 色偷偷av一区二区三区| 国产一区二区在线观看app| 高清国产精品人妻一区二区| 精品国产一区二区22| 中文字幕日本精品一区二区三区| 秋霞午夜一区二区| 97久久精品无码一区二区| 日韩AV无码一区二区三区不卡 | 亚洲av午夜精品一区二区三区|