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IS480 Team wiki: 2017T1 Ravenous Final Wiki

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Project Progress Summary

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As of 14th November 2017, we have completed 41% of iteration 9 and this is our last iteration.

To view final slides, click here
To access EvBot & FaBot, login to Workplace@Facebook instance by clicking here
To access Dashy (our instance), click here
To access Dashy (WOG's live instance), click here

To view handover documentation, click here

For more information on how to access our applications, please view Quality of Product > Deployment Section

Project Highlights & Snapshots

Ravenous - Final Project Highlights.png

Project Management

Project Status

The three diagrams below show the progress of our project, a high level overview of completion of project modules and a high level overview of change in scope to our project modules respectively.

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Ravenous - FinalFunctionalitiesSummary.png



Ravenous - FinalFunctionalitiesChange1.png

Scope: Core

Module Task New Feature? Status Confidence level Comments
Event ChatBot (Organiser) Register an event No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organiser) Remove event No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organiser) Close event No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organiser) Add & Remove questions No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organiser) Send Survey Questions No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organiser) Send Survey Reminder to participants No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organiser) View Survey Questions No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organiser) View Event Snapshot No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organiser) About EvBot No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Participants) Check-in Event Attendance No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Participants) View Surveys No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Participants) About EvBot No Fully deployed and 100% Tested; On production server 1.0 Completed
Facility Booking ChatBot & FMS Module I Search available facilities No Fully deployed and 100% Tested; On production server 1.0 Completed
Facility Booking ChatBot & FMS Module I Book available facilities No Fully deployed and 100% Tested; On production server 1.0 Completed
Facility Booking ChatBot & FMS Module I View booked facilities No Fully deployed and 100% Tested; On production server 1.0 Completed
Facility Booking ChatBot & FMS Module I Cancel booked facilities No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module I View claim rate vs active rate for all agencies and specific agency chart No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module I View aggregated engagement scores of groups within an agency No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module I View breakdown of engagement scores of groups within an agency No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module I View Pc vs Mobile Graph No Delivered 1.0 Completed
Analytics Dashboard Module II View group privacy setting across specific group charts No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module II View group activity charts No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module II View interaction analysis of specific agency No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module III View number of active users charts No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module III View content on workplace chart No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module III View word cloud chart No Delivered 1.0 Completed
Analytics Dashboard Module III View post-time & comment-time chart No Fully deployed and 100% Tested; On production server 1.0 Completed
Authentication & Security Module Register account on Dashy OTP Bot No Fully deployed and 100% Tested; On production server 1.0 Completed
Authentication & Security Module OTP Bot No Fully deployed and 100% Tested; On production server 1.0 Completed
Authentication & Security Module Login/Logout on Dashy No Fully deployed and 100% Tested; On production server 1.0 Completed
Authentication & Security Module Forget password No Fully deployed and 100% Tested; On production server 1.0 Completed
Authentication & Security Module Secure all API Calls with JWT Token No Fully deployed and 100% Tested; On production server 1.0 Completed

Scope: Secondary

Module Task New Feature? Status Confidence level Comments
Analytics Dashboard Module IV Export dashboard to CSV File No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module IV Filtering options based on user activity/profile No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module IV Dashy additional metrics: Overview, posts/comments of previous day vs last week’s same day No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module IV Dashy additional metrics: Group, Measure posts, comments and reactions in a given period of time and see the most popular days and times that members engage. (advanced filtering) No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module IV Dashy additional metrics: Most seen posts by number of seen per group Yes Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Module IV Dashy additional metrics: Top contributor by agency Yes Fully deployed and 100% Tested; On production server 1.0 Completed
FMS Module II Release a booking that has begun No Fully deployed and 100% Tested; On production server 1.0 Completed
FMS Module II Remind and extend a booking that has begun No Fully deployed and 100% Tested; On production server 1.0 Completed
FMS Module II Modify make, delete, view and search facilities to replicate GovTech's System No Fully deployed and 100% Tested; On production server 1.0 Completed
Facility Booking ChatBot Module II Advanced NLP No Fully deployed and 100% Tested; On production server 1.0 Completed
Facility Booking ChatBot Module II View room host details on workplace and have the ability to contact host No Fully deployed and 100% Tested; On production server 1.0 Completed
Facility Booking ChatBot Module II Re-prompt user with other room to book in case of clash in booking No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard and Bots Integration Module Expose EvBot Event report No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard and Bots Integration Module Dashy displays event statistics for event organisers No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard and Bots Integration Module Expose FaBot usage metrics No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard and Bots Integration Module Expose EvBot usage metrics No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard and Bots Integration Module View FaBot usage metrics for workplace managers No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard and Bots Integration Module View EvBot usage metrics for workplace managers No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organisers) II Display attendee that didn't turn up No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organisers) II Organisers message broadcast No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organisers) II Organisers trigger prompt for participants to check in No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organisers) II Display attendees yet to complete survey No Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Mobile Responsiveness Optimize web pages No Fully deployed and 100% Tested; On production server 1.0 Completed

Scope: Good to have

Module Task New Feature? Status Confidence level Comments
Database Optimization Module Optimize syncing of new data from Workplace@FB with Crunchy's database No Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organisers) III Export Event Report as CSV No Fully deployed and 100% Tested; On production server 1.0 Completed


Project Schedule

This section shows a low level overview of our tasks in each iteration before midterm and current point (final)

Planned

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Actual

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Project Metrics

The chart shows the number of completed tasks against number of assigned tasks for all our iterations so far. The bigger the circle, the higher the percentage of task metrics

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The charts below show Total Bug Score per iteration, Total Number of Bugs per iteration and Average Bug Score per Bug per iteration respectively.

Ravenous - Total Bug Score.png Ravenous - Total Bug Num.png

Project Risks

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From the period of Mid Term (9/10/2017) to Final presentation, we would like to highlight these two risks as area of concerns.

S/N Risk Type Risk Description Likelihood Impact Level Risk Grade Strategy Adopted Action
8 Technical Risk Our project is heavily dependent on Facebook API that we have no control over. High High A Mitigation Our team is part of the Multi-Company Group with PSD, MOE and Facebook. We are in touch with Rohan who we can approach if we have any problem with the Workplace instance
11 Technical Risk Team is given a relatively small server to deploy on the government instance. Thus, code has to be optimized in order the application to work. High High A Mitigation Developers to learn and understand how to optimize code through online resources


From the period of Mid Term (9/10/2017) to Final presentation, we have a total of 3 change requests.

S/N Application, Requested by Change description Impact on Schedule Technical Complexity Business Value/Reason for Request Score Action Taken Status of Request
38 Dashy, Sponsor Top Contributor in Agency 2 2 High, Allows Workplace Managers to quickly know who are the top contributor in their agency. Currently, workplace default dashboard only allow them to view top contributor of whole of government 4, High Accept change request and team discuss the most appropriate iteration to implement the change request Closed
39 Dashy, Sponsor Average seen rate of all groups in agency 2 2 High, It allows Workplace Managers to quickly know how many posts are being viewed by how many their users in their agency, hence this supplements them in knowing activity rate in their agency 4, High Accept change request and team discuss the most appropriate iteration to implement the change request Closed
40 Dashy, Sponsor Deploy on WOG AWS server 3 3 High, Allow the team to deploy for live usage 6, High Accept change request and team discuss the most appropriate iteration to implement the change request Closed

Technical Complexity

Technical complexity 1 - Deployment Server Memory

We had to work with an Amazon EC2 T2.micro instance to host our analytics processing server. The T2.Micro instance only provides 1gb memory and the processing server is expected to load an expected volume of 1 million data from the Whole Of Government Workplace instance.

The initial task of populating our database was a challenge as we constantly reached our memory limit while trying to retrieve and store the data from Facebook Workplace, into our database. Upgrading the instance to a better one was an option but we decided to optimize the memory usage of our code first.

(i) Bluebird Promises
During our mid-term presentation, we mentioned that we used the Native Promise library to approach our asynchronous issues. Bluebird promises work the same say as Node’s native Promise however, it is less memory intensive. The benchmark results can be found on bluebird’s documentation but we decided to run some tests ourselves. Here is the score from the tests we ran using Node’s process.memoryUsage() function:

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Through our results, the heapUsed for the bluebird library is lesser than the native Promise, confirming the benchmark scores found online. Thus, we refactored the promises that were used by memory intensive functions to make use of the bluebird library


(ii) Node Garbage Collector
To free up memory within the code, we had to make use of Node’s garbage collector which runs in the background. The garbage collector frees up memory when the objects have no reference to any other objects. In order to reduce the memory usage of our code, we explicitly force node’s garbage collector to remove objects that are unused by assigning a null value to the object before ending the function.

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(iii) Mongo Cursor and Buffer (Managing Memory Usage)
The usage of cursor is similar to streaming data. Instead of fetching all the data from the database and consuming it all at once, cursor streams the data to our server.

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After streaming the data, we manually implemented our own Buffer to batch process the records that were streamed. With the combination of Mongo’s cursor and our buffer, it helped us to manage the memory usage of our application.


(iv) EcmaScript6 (ES6)’s let and CONST declaration
We realized that with the new declarations of let and CONST, it uses less memory than the old global declaration ‘var’. We refactored our codes to make use of only ES6’s let and CONST declaration.


Technical complexity 2 - Code Optimization to improve Runtime

Since we had to process approximately a million data, our codes have to run quickly. One of the reasons why Node.js was chosen as the language for this task, is for this very purpose.

(i) Async Parallel

With the new version of Node.js (v7.6 and above), came the native async library. We took advantage of this library by making asynchronous queries to our database so that database queries can run in parallel.

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In the simple example above, two functions (calls) are made to the database. However, with the async.parallel function, they are made at the same time. Only when both queries have finished executing, will the code continue with the results of both queries. This helps to improve the runtime processing speed when widely used across the code.


(ii) Native Mongo Queries

Although the library that we used to interact with MongoDB, named Mongoose, has certain functions to aid developers in using a MongoDB, it is limited. The general find() or findOneAndUpdate() queries are insufficient for more complex queries. Therefore, an easy but inefficient solution is to find all records, and sift out the records we need by iterating through the records. This is extremely inefficient as compared to using the native queries.

Therefore, we had to learn Mongo Queries to improve the speed of our application. We made use of Mongoose’s aggregate function include in Mongo’s operators to perform some of the more complex queries.

Some of the operators that we used include:$match, $group, $project, $sort, $count

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Technical complexity 3 - Making Large Number of API Calls to Workplace Graph API

As of 1st November 2017, there are a little more than 150,000 public servants on the Whole of Government Workplace instance. Retrieving every detail on these accounts would result in a few hundreds of thousand requests to Workplace’s API.

This was an issue initially as we were met with lots of rate limiting and throttle error that were set in Workplace’s Graph API. This limits us to about 200 calls per hour per user in aggregate. We adopted some techniques to overcome this issue:

(i) Bluebird Concurrency Control (Promise.Map)
Bluebird’s Promise.Map allowed us to manage the number of requests we made to Workplace API at any moment.

Ravenous - ftc3a.png


The code example above limits us to make only 10 concurrent calls to the ‘populatePostsForGroup’ function. In this function, requests are made to Workplace Graph API, instead of just shooting thousands of requests to Workplace Graph API in a matter of seconds.

Furthermore, the concurrency control in bluebird is able continuously make calls at the specified rate of 10. For example, when two requests are done, the queued requests will send an additional two to Graph API.

In essence, the concurrency control balances between fast API calls and API rate limit errors.

(ii) Request Retry
Since hundreds of thousand API calls have to be made, there are bound to be failures in some of the calls. During our test phase, we encountered some of failures with minimal explanation. After recommendation from our Sponsor’s Tech team, we concluded that the best way to solve this error, is to just try sending the request again.

To do this, we made use of the requestretry library on NPM.

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The library helped us to easily resend the request to whenever an error is returned in the response. From the above options set, the request will keep trying at a maximum attempt of 5 times. After the 5th attempt is exhausted, it will pause the retry for five seconds before continuing to try.

This helped us to overcome the unknown Workplace API errors and allowed us to continue retrieve data from Workplace reliably.


Technical complexity 4 - Real-Time Data (New)

With the new Webhook subscription feature offered by Workplace Graph API, it allows us to update our database data in real-time.

In order to listen to these events from Workplace, we had to set up our own webhooks. The webhooks were set up using Workplace’s interface.

The topics our webhooks are subscribed to are: Posts (New and Modified), Comments (New and Modified), Users (New and Deleted), Events (New)

Ravenous - ftc4a.png


Every time a new event occurs, for example a user just posted a new comment, Workplace will send us the payload to the subscribed webhook accordingly. We will then handle the events accordingly.


Quality of Product

Intermediate Deliverables

Topic of Interest Link
Project Management Schedule & Functionalities
Metrics
Risk Management
Change Management
Minutes
Project Documentation Technical Diagrams
Prototype
Persona & Scenario
Testing Bug Log
Internal Testing & Test Cases
UT1
UT2
UT3

Deployment

The deployment links are as follow:

We have 3 versions of Dashy for different purposes. Staging refers to application in the development phase and production refers to applications are that production ready. WOG Live instance is the one we deploy on WOG's server. Only these three applications have a user interface.


These are the backend servers that support our applications on Workplace@FB. They do not have a user interface.


Our Workplace@FB instance can be found here: https://psd-test.facebook.com/

- Please note that by logging into our Workplace@FB instance, you will have access to EvBot and FaBot.
- You will need to use the production link above to access Dashy.

You may login to our Workplace@FB instance using the credentials below. You may also login to Dashy using the same credentials
Email login id: wiki_user@test.sis.smu.edu.sg
Password: wiki_user123

Testing

Team Ravenous has conducted a total of 3 User Testings.
The first user testing placed emphasize on whether users know how to use our applications, whether they understand the flow of tasks and observe how they interact with our ChatBots.
The second user test is about the usability improvement that we have made since user testing 1.
The third user test is about the usability improvement that we have made since user testing 2.

The results of UT1 can be found here
The results of UT2 can be found here
The results of UT3 can be found here

Details of internal testing can be found in the deliverable table above.

Reflection

Team Reflection

This project was an exciting and enriching journey for Team Ravenous. Collectively, we not only gain new knowledge and skills about our roles, we also get to expand our professional network. Through this project, we get to meet and interact with PSD Technical Team, GovTech Team as well as business & IT professionals from 6 different government agencies. Through the interactions, we know why are certain processes carried out this way, why they are doing this and so on.

Individual Reflection

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