UBC CPSC310-20W1: Intro to SE CHECKPOINT 3 (北美程序代写,加拿大程序代写,University of British Columbia代写,CSSE7201代写,TypeScript代写)

You will adapt your existing InsightFacade to also be accessed using REST endpoints

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本次CS代写的主要涉及如下领域: 北美程序代写,加拿大程序代写,University of British Columbia代写,CSSE7201代写,TypeScript代写








There are two major tasks for this checkpoint:

  • Implementing a Web Server and a Frontend UI

  • Solving the course timetabling problem

This checkpoint will extend your solutions for C1 and C2 so you must continue to work with the same partner using the same repository. You will not have to hand anything in; we will automatically analyze your repo on every push between when the checkpoint is released and the due date specified here.

Important notes:

  • Autobot will create a pull request in your repos during the first week of this sprint. It will contain some changes to your project (boilerplate frontend and server implementations) to transform your software into a Web app. It is a very good idea to carefully look at this pull request and what Autobot wants to merge into your projects.

  • This spec should be considered a high level outline for this checkpoint because it would be very easy to get lost in the details. While the pull request from Autobot will make things more clear, it is also more important than ever to attend the in-class tutorial where we will go through the in more detail.

  • There may be more pull requests from Autobot during this checkpoint because we may need to change things on the go. Please keep an open eye on Piazza and merge all pull requests sooner rather than later to avoid conflicts.




  • Updated the Course Timetabling Problem/Assumptions subsection




Checkpoint 1 built a query engine to answer queries about UBC course sections. Checkpoint 2 extended the query engine to aggregate answers about actual courses and support details about course spaces on campus. Ultimately, you have built a robust backend for quickly and flexibly querying a diverse dataset. This is a great foundation, but is not particularly usable: how could your implementation actually be used in the real world? After all, software is of little value if it is never deployed and used, no matter how well it is designed and implemented.

In this checkpoint you will complement your backend with a Web server and a frontend UI such that your software can be deployed and used as a Web application. Just as is common practice in Web development, you will be given a boilerplate UI implementation (consider us the "designers"). Most of the functionality has been removed from the sources and it will be your job to hook it all up with your Web server implementation.






Bootstrap code

Once you merge the pull request from Autobot, you'll have three new files in your project associated with the implementation of a Web server:

  • src/App.ts contains the source code for starting the application and initializing the server. This will be given to you for free.

  • src/rest/Server.ts contains the logic for your server.

  • test/Server.spec.ts contains the tests for your server.

Both the Server.ts and Server.spec.ts files will contain some sample code to point you in the right direction. We will use restify as a REST server library. Please refer to its documentation first whenever questions arise.



REST Endpoints

You will adapt your existing InsightFacade to also be accessed using REST endpoints. Both InsightFacade and the REST endpoints must continue to work independently.

  • GET / returns the frontend UI; this will already be implemented for you.

  • PUT /dataset/:id/:kind allows to submit a zip file that will be parsed and used for future queries. The zip file content will be sent 'raw' as a buffer, you will need to convert it to base64 server side.

    • Response Codes:

      • 200: When InsightFacade.addDataset() resolves.

      • 400: When InsightFacade.addDataset() rejects.

    • Response Body:

      • {result: arr}: Where arr is the array returned by a resolved addDataset.

      • {error: err}: Where err is a string error message from a rejected addDataset. The specific string is not tested.

  • DELETE /dataset/:id deletes the existing dataset stored. This will delete both disk and memory caches for the dataset for the id meaning that subsequent queries for that id should fail unless a new PUT happens first.

    • Response Codes:

      • 200: When InsightFacade.removeDataset() resolves.

      • 400: When InsightFacade.removeDataset() rejects with InsightError.

      • 404: When InsightFacade.removeDataset() rejects with NotFoundError.

    • Response Body:

      • {result: str}: Where str is the string returned by a resolved removeDataset.

      • {error: err}: Where err is a string error message from a rejected removeDataset. The specific string is not tested.

  • POST /query sends the query to the application. The query will be in JSON format in the post body.

    • NOTE: the server may be shutdown between the PUT and the POST. This endpoint should always check for a persisted data structure on disk before returning a missing dataset error.

    • Response Codes:

      • 200: When InsightFacade.performQuery() resolves.

      • 400: When InsightFacade.performQuery() rejects.

    • Response Body:

      • {result: arr}: Where arr is the array returned by a resolved performQuery.

      • {error: err}: Where err is a string error message from a rejected performQuery. The specific string is not tested.

  • GET /datasets returns a list of datasets that were added.

    • Response Codes:

      • 200: When InsightFacade.listDatasets() resolves.

    • Response Body:

      • {result: arr}: Where arr is the array returned by a resolved listDataset

Other GET/* endpoints will serve static resources. This will already be implemented in the bootstrap as well.

The :id and :kind portions above represent variable names that are extracted from the endpoint URL. For the PUT example URL http://localhost:4321/dataset/mycourses/courses, mycourses would be the id and courses would be the kind.

Note: Those of you paying attention in lecture may notice that this is not a very RESTful API. An an exercise, consider how you could modify one of the endpoints to make a more RESTful API!




The same libraries and frameworks as before (Mocha, Chai) will be used for testing. This time, however, your tests will have to send requests to your backend and check the received responses for validity. The bootstrap code in test/Server.spec.ts will point you in the right direction.

Note: The response formats for the 2XX and 4XX cases may look slightly different. You should use the debugger to inspect the responses to determine where to find the values you want to test.



Starting and accessing the app

A new yarn command yarn start will be available in your project through a change to package.json. It will essentially run App.js as a node application. Once you started the server, you'll be able to access the app in the browser at http://localhost:4321. Please note that your datasets must be available for the UI to work. You could find a way to call addDataset using one of your endpoints, or keep a cached copy of your data around and just paste it into your ./data folder so the server can load it from disk.






Bootstrap code

Once you merge the pull request from Autobot, you'll have a new directory frontend/ in your repo that contains the boilerplate frontend implementation. The subdirectory public contains the static sources that will be hosted by your Web app:

  • index.html contains the HTML code for the UI. This file is hosted by the GET / endpoint of your REST server.

  • bundle.css contains the styles for the UI.

  • bundle.js contains the existing logic for the UI.

  • query-builder.js will contain the logic for building queries from the UI.

  • query-sender.js will contain the logic for sending queries from the UI to your Web server.

  • query-index.js will contain the logic for the chain of building a query and sending it to the UI once the form in the UI is submitted.

Please note that you should only touch the query-*.js files for your frontend implementation. Each of the three files is described in Implementation section below. The other parts are given to you and should not be changed.




The frontend part of this checkpoint differs from your previous development in several ways. The two most significant are:

  1. Plain JavaScript. While it is theoretically possible to develop in TypeScript on the frontend as well, it is not common practice and we will stick to plain JavaScript here. Please apply to this rule and don't try to make TypeScript work somehow within the frontend/ directory. The advantage is that you won't have to build/compile your project when you work on the frontend.

  2. Browser. You will dive into the world of browsers with your frontend implementation. Your frontend code will be run client-side in the browser and will communicate with your Web server via REST/Ajax calls. This means also that you will have the global window, document and XMLHttpRequest objects from the browser available anywhere in your code.

The source code of the UI merged into your repository will expose a global object CampusExplorer on the browser's window object that contains three methods:

  • CampusExplorer.buildQuery builds queries from the current state of the UI. Information from the UI must be extracted using the browser-native global document object. The returned queries must be of the same format as the ones given to your InsightFacade.performQuery method.

  • CampusExplorer.sendQuery sends an Ajax/REST request to the POST /query endpoint of your Web server, taking a query as produced by CampusExplorer.buildQuery as argument. You must use the browser-native XMLHttpRequest object and its send and onload methods to send requests because otherwise the Autobot tests will fail.

  • CampusExplorer.renderResult renders a given result from the POST /query endpoint in the UI.

The last of the above methods (CampusExplorer.renderResult) will be already available for you. It will be your job to implement the other methods in the respective files frontend/public/query-builder.js and frontend/public/query-sender.js.

Once these methods are implemented, you will have to attach them to the submit button in the UI and call them in the right chain in frontend/public/query-index.js. Note that index is not directly tested, but will help you test/see your final project come together (and requires very little code to make work). The sequence to implement this is as follows:

  1. Listen for clicks on the submit button on the UI. When that happens:

  2. Query is extracted from UI using your implemented CampusExplorer.buildQuery method.

  3. Query is sent to the POST /query endpoint using your implemented CampusExplorer.sendQuery method.

  4. Result is rendered in the UI by calling CampusExplorer.renderResult with the response object from the endpoint as argument.

More specific directions will be provided as comments in the bootstrap files.

There are a few important notes on CampusExplorer.buildQuery. Please consider these carefully because otherwise Autobot tests may fail.

  • The UI will only be able to build a subset of all possible queries. Several complex structures (e.g. nesting) are not possible and this is intended.

  • If no conditions are specified, the query will have no conditions.

  • If only one condition is specified, no logic connector (and/or) should be used but only the single condition.

  • You can select multiple items for columns and order in the UI. This is typically done using Ctrl+Click on Windows and Linux, or Command+Click on Mac.

  • The order of the keys in the order section is ignored and will not be tested by Autobot.

  • If the sort is ascending with a single key, C1 or C2 sort can be used.

  • As there is no location to specify a dataset id, you can assume that the id will be either courses or rooms as appropriate.

Important note: usage of any library not native to the browser is strictly prohibited in the frontend part of this checkpoint. Please stick to the global CampusExplorer and document objects, and the XMLHttpRequest class, which are the only ones required. Autobot will fail if you violate this requirement.

Just to make sure: for the frontend implementation, please only touch the files query-builder.js, query-sender.js and query-index.js in your frontend/public/ directory. The other parts are already implemented, hooked up and ready to go in the bootstrap sources.




We will use the test runner Karma for frontend testing. package.json will change respectively to accommodate the required dependencies. Karma works in a way that it will run your tests in a browser environment. There will be a new command yarn test:frontend in your project that will run the frontend test suites with Karma.

The configuration for Karma is in karma.conf.js. Please do not change this file because it may make Autobot fail running the tests. However, you can create your own configuration file (e.g. karma.my.conf.js) and run Karma explicitly with your file: ./node_modules/karma/bin/karma start karma.my.conf.js. Karma is a powerful tool and can be run with different browsers, from the terminal or IDE and you can even hook up the WebStorm debugger with Chrome using the JetBrains IDE Support add-on. But you can also debug your frontend code simply using the developer tools of your browser.

There will be two test suites for C3:

  • query-builder.spec.js contains the test suite for CampusExplorer.buildQuery.

  • query-sender.spec.js contains the test suite for CampusExplorer.sendQuery.

Rather than editing these test suites directly, you will work with so-called HTML and JSON fixtures in the frontend/test/fixtures/ directory. For testing your two methods buildQuery and sendQuery you should maintain a set of HTML files in fixtures/html, set of named query objects in queries.json and a set of descriptions in descriptions.json. fixtures/html should contain one HTML file named [queryName].html for each query with the HTML code of the currently active form in the UI as content. To create these HTML fixtures, the UI contains a Copy HTML button that will copy the current HTML of the active form element in the UI to your clipboard which you can then simply paste into your HTML files. queries.json should contain a queryName to query mapping with the queries that you expect for the HTML code in [queryName].html. The idea is this: "if the HTML in the UI looks like [queryName].html, then I expect the query with key [queryName] in queries.json to be returned by CampusExplorer.buildQuery". This way you can create your test scenarios by interacting with the UI and then create your HTML fixtures. We included an example in the bootstrap code to get a better understanding. The query-sender.spec.js test suite will then take every query fixture you specified in queries.json and check if your CampusExplorer.sendQuery method sends an Ajax request properly. descriptions.json should contain a description for each query with a mapping queryName to description.

While you must not edit the actual test suites query-builder.spec.js or query-sender.spec.js directly, we highly encourage you to look at the code. We implemented a few utility methods for testing on a global window object TTT, as well as two new chai assertions equalQuery and sendAjaxRequest. Understanding how the test suites work will help you understand the test framework better and will generally help you with the frontend part of this checkpoint.

Just to make sure: for frontend testing in this checkpoint, it is sufficient to add your HTML fixtures in frontend/test/fixtures/html/, your expected queries in frontend/test/fixtures/queries.json and query descriptions in frontend/test/fixtures/descriptions.json. No other files need to be changed.







In this checkpoint, you will also solve a simplified version of the problem of scheduling university courses. Develop a schedule function that schedules course sections into rooms and timeslots, subject to constraints defined below.

export interface SchedSection {

courses_dept: string;

courses_id: string;

courses_uuid: string;

courses_pass: number;

courses_fail: number;

courses_audit: number;

courses_avg?: number;

courses_instructor?: string;

courses_title?: string;

courses_year?: number;



export interface SchedRoom {

rooms_shortname: string;

rooms_number: string;

rooms_seats: number;

rooms_lat: number;

rooms_lon: number;

rooms_name?: string;

rooms_fullname?: string;

rooms_address?: string;

rooms_type?: string;

rooms_furniture?: string;

rooms_href?: string;



export type TimeSlot =

"MWF 0800-0900" | "MWF 0900-1000" | "MWF 1000-1100" |

"MWF 1100-1200" | "MWF 1200-1300" | "MWF 1300-1400" |

"MWF 1400-1500" | "MWF 1500-1600" | "MWF 1600-1700" |

"TR 0800-0930" | "TR 0930-1100" | "TR 1100-1230" |

"TR 1230-1400" | "TR 1400-1530" | "TR 1530-1700";


export interface IScheduler {


* Schedule course sections into rooms


* @param sections

* An array of course sections to be scheduled


* @param rooms

* An array of rooms for sections to be scheduled into


* @return Array<[SchedRoom, SchedSection, TimeSlot]>

* return a timetable, which is an array of [room, section, time slot] assignment tuples


schedule(sections: SchedSection[], rooms: SchedRoom[]): Array<[SchedRoom, SchedSection, TimeSlot]>;




Hard Constraints

  • The schedule function must not return a timetable with values for sections and rooms that are not given in the input, or a timetable with invalid time slots

  • A section must fit into the room in order to be scheduled. The size (i.e. enrolment) of the section, which is the sum of courses_pass, courses_fail, and courses_audit values, must be less than or equal to the size of the room, which is the value of rooms_seats

  • No two sections can be scheduled in the same room at the same time

  • No two sections of the same course can be scheduled during overlapping timeslots

  • Valid time slots are defined by TimeSlot (15 in total). A scheduled section takes up either 1h on MWF (Monday, Wednesday, Friday), or 1.5h on TR (Tuesday, Thursday)

  • A section can be scheduled at most once



Soft Constraints

A timetable is considered valid if it satisfies all the hard constraints. However, such a timetable may not be a good timetable. For example, though useless, [] is a valid timetable. Thus we also want some measures to evaluate the "goodness" of the timetable returned by calling schedule.

From the university's perspective: want to enrol as many students as possible

From the students' perspective: want courses to be scheduled inside buildings that are close together (less walking)

So your schedule function should aim to:

  • Maximize scheduled enrolment

  • Minimize the distance between the two buildings that are furthest apart among the buildings used in the timetable

(see the "Grading" section further down for exact definitions and calculations)




  • The input sections and rooms do not contain duplicates (i.e. you can treat them as sets)

  • The input sections and rooms each contains at least one item

  • The scale of the sections input will not exceed 1,000 sections (while ideally your implementation would handle an arbitrary input from the courses dataset, to allow for a large variety of algorithms and solutions we have restricted the input size)



Example Input

let sections = [


"courses_dept": "cpsc",

"courses_id": "340",

"courses_uuid": "1319",

"courses_pass": 101,

"courses_fail": 7,

"courses_audit": 2



"courses_dept": "cpsc",

"courses_id": "340",

"courses_uuid": "3397",

"courses_pass": 171,

"courses_fail": 3,

"courses_audit": 1



"courses_dept": "cpsc",

"courses_id": "344",

"courses_uuid": "62413",

"courses_pass": 93,

"courses_fail": 2,

"courses_audit": 0



"courses_dept": "cpsc",

"courses_id": "344",

"courses_uuid": "72385",

"courses_pass": 43,

"courses_fail": 1,

"courses_audit": 0




let rooms = [


"rooms_shortname": "AERL",

"rooms_number": "120",

"rooms_seats": 144,

"rooms_lat": 49.26372,

"rooms_lon": -123.25099



"rooms_shortname": "ALRD",

"rooms_number": "105",

"rooms_seats": 94,

"rooms_lat": 49.2699,

"rooms_lon": -123.25318



"rooms_shortname": "ANGU",

"rooms_number": "098",

"rooms_seats": 260,

"rooms_lat": 49.26486,

"rooms_lon": -123.25364



"rooms_shortname": "BUCH",

"rooms_number": "A101",

"rooms_seats": 275,

"rooms_lat": 49.26826,

"rooms_lon": -123.25468





Example Output

Note: the output timetable shown here is just one of the possibilities for this small example, try to convince yourself of this idea by finding another timetable.

[ [ { rooms_shortname: 'AERL',

rooms_number: '120',

rooms_seats: 144,

rooms_lat: 49.26372,

rooms_lon: -123.25099 },

{ courses_dept: 'cpsc',

courses_id: '340',

courses_uuid: '1319',

courses_pass: 101,

courses_fail: 7,

courses_audit: 2 },

'MWF 0800-0900' ],

[ { rooms_shortname: 'ANGU',

rooms_number: '098',

rooms_seats: 260,

rooms_lat: 49.26486,

rooms_lon: -123.25364 },

{ courses_dept: 'cpsc',

courses_id: '340',

courses_uuid: '3397',

courses_pass: 171,

courses_fail: 3,

courses_audit: 1 },

'MWF 0900-1000' ],

[ { rooms_shortname: 'BUCH',

rooms_number: 'A101',

rooms_seats: 275,

rooms_lat: 49.26826,

rooms_lon: -123.25468 },

{ courses_dept: 'cpsc',

courses_id: '344',

courses_uuid: '62413',

courses_pass: 93,

courses_fail: 2,

courses_audit: 0 },

'MWF 0800-0900' ],

[ { rooms_shortname: 'ALRD',

rooms_number: '105',

rooms_seats: 94,

rooms_lat: 49.2699,

rooms_lon: -123.25318 },

{ courses_dept: 'cpsc',

courses_id: '344',

courses_uuid: '72385',

courses_pass: 43,

courses_fail: 1,

courses_audit: 0 },

'MWF 0900-1000' ] ]




The performance of your schedule function will be evaluated over multiple test cases. Performance for each test case will be based on an enrolment score E and a distance score D.

E is calculated as scheduled enrolment divided by total enrolment.

Scheduled enrolment is the sum of the sizes of all valid scheduled sections in the timetable returned by schedule.

Total enrolment is the sum of the sizes of all input sections.

D is calculated as the great-circle distance in meters between the two buildings that are furthest apart among the buildings used in the timetable returned by schedule, using their latitudes and longitudes, divided by 1372 meters.


  • If no building is used in the timetable, then D is Infinity

  • If only 1 building is used in the timetable, then D is 0

  • We use the haversine formula to calculate the great-circle distance between buildings using their latitudes and longitudes

  • Both E and D should be a number between 0 and 1

  • The distance between ANSO and OSBO is 1372 meters, and it is the maximum possible distance between any 2 buildings in the rooms dataset

The schedule function should aim to maximize E and minimize D while satisfying all the constraints at the same time.

The pseudocode for grading:

for each test_case:

table_stu = timetable returned running your version of schedule() on test input

E1, D1 = evaluate table_stu to compute scores

table_ref = timetable returned by running reference solution schedule() on test input

E2, D2 = evaluate table_ref to compute scores

if (0.7 * E1 + 0.3 * (1 - D1) + test_case.DELTA > 0.7 * E2 + 0.3 * (1 - D2))




Note: DELTA is a per-test value.




Our aim with this checkpoint is to give you an impression on Web/frontend development without confronting you with extensive details and fiddling of implementing a user interface. That being said, hooking together the architecture and implementing the required parts will still be time consuming. This specification may seem rather unstructured to you. This is intended since things are often unstructured in the world of Web development and creating something structured out of an unstructured specification certainly makes a good developer. Most likely, you will spend the most time understanding how the pieces of the puzzle fit together in this checkpoint.

There is no best way to get started, but a few hints may help you:

  • Create the big picture of your project in your mind. Use pencil and paper and draw some diagrams. Where is what component and how do they interact with each other? What's on the client and what's on the server?

  • The two parts in this checkpoint (server, frontend) should be implemented and tested independent from each other. They are only hooked together with Ajax requests going from the frontend to the server but all parts are designed to be implemented and tested independently.

  • Make use of the queries you used for testing in the previous checkpoints. You'll want to keep a clear thread throughout your whole project. (Note though that not all queries can be produced with the UI.)

  • Carefully look at the bootstrap code and simply run the Web app with only the bootstrap sources. If this spec looks intimidating to you, the actual bootstrap sources might enlighten you how everything fits together.

Good luck and we sincerely hope you'll also have some fun implementing this checkpoint!




A survey will be required to detail your contribution to your group's project. This will involve a mandatory survey. Failure to submit the survey by the final deadline will cap your retrospective multiplier at 0.8.




AutoTest scoring will be based on:

  • Functional Correctness of the Web Server + UI.

  • Optimality of the Scheduler.