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IS480 Team wiki:2017T1 Ravenous Mid Term Wiki

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Project Progress Summary

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As of 9th October 2017, we have completed 85% of iteration 7 and we have 2 more iterations to go.

To view midterm slides, click here
To access EvBot & FaBot, login to Workplace@Facebook instance by clicking here
To access Dashy, click here

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

Project Highlights & Snapshots

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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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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 Fully deployed and 100% Tested; On production server 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 Yes Fully deployed and 100% Tested; On production server 1.0 Completed
Authentication & Security Module OTP Bot Yes Fully deployed and 100% Tested; On production server 1.0 Completed
Authentication & Security Module Login/Logout on Dashy Yes Fully deployed and 100% Tested; On production server 1.0 Completed
Authentication & Security Module Forget password Yes Fully deployed and 100% Tested; On production server 1.0 Completed
Authentication & Security Module Secure all API Calls with JWT Token Yes 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 Yes To be completed in iteration 8 1.0 To be completed in iteration 8
Analytics Dashboard Module IV List of activated and deactivated accounts of each agency in past 7 days No In Progress 1.0 In Progress
Analytics Dashboard Module IV Filtering options based on user activity/profile No In Progress 1.0 In Progress
Analytics Dashboard Module IV Dashy additional metrics: Overview Yes In Progress 1.0 In Progress
Analytics Dashboard Module IV Dashy additional metrics: Group Yes To be completed in iteration 8 1.0 To be completed in iteration 8
FMS Module II Release a booking that has begun Yes In Progress 1.0 In Progress
FMS Module II Extend a booking that has begun Yes In Progress 1.0 In Progress
FMS Module II Modify make, delete, view and search facilities to replicate GovTech's System Yes In Progress 1.0 In Progress
Facility Booking ChatBot Module II Advanced NLP Yes 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 Yes Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard and Bots Integration Module Expose EvBot Event report Yes Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard and Bots Integration Module Dashy displays event statistics for event organisers Yes Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard and Bots Integration Module Expose FaBot usage metrics Yes In Progress 1.0 In Progress
Analytics Dashboard and Bots Integration Module Expose EvBot usage metrics Yes Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard and Bots Integration Module View FaBot usage metrics for workplace managers Yes In Progress 1.0 In Progress
Analytics Dashboard and Bots Integration Module View EvBot usage metrics for workplace managers Yes 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 Yes Fully deployed and 100% Tested; On production server 1.0 Completed
Event ChatBot (Organisers) II Display attendees yet to complete survey Yes Fully deployed and 100% Tested; On production server 1.0 Completed
Analytics Dashboard Mobile Responsiveness Optimize web pages Yes 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 Yes To be completed in iteration 8 1.0 To be completed in iteration 8
Database Optimization Module Separate Database into transactional and analytical for all applications Yes To be completed in iteration 8 1.0 To be completed in iteration 8
Event ChatBot (Organisers) III Word cloud on survey results Yes To be completed in iteration 8 1.0 To be completed in iteration 8
Event ChatBot (Organisers) III Export Event Report as CSV Yes To be completed in iteration 8 1.0 To be completed in iteration 8


Project Schedule

This section shows a low level overview of our tasks in each iteration before acceptance and current point (mid term)

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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From Iteration 0 to 5, we keep track of task assignment date, expected due date and actual completion date. From there, we carry out task completion date analysis, individual task assignment analysis and group task assignment analysis. However, from iteration 6 onward, we made an improvement to our project management and keep track of expected time spent per task as well as actual time spent per task.

Ravenous Iteration analysis 5 Ravenous Iteration analysis 6


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

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Project Risks

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From the period of Acceptance (18/8/2016) to Mid-Term 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
10 Project Management Risk GovTech is keen to share their API with us for us to explore the potential of our applications on government platform. However, the API from GovTech is only available in late Oct/early Nov High High A Mitigation We are intending to replicate some of the functions that GovTech applications have. This can not only prepare us for possible integration but also tap on their knowledge to create functions that users want.


From the period of Acceptance (18/8/2016) to Mid-Term presentation, we have a total of 29 change requests. Out of the 29 change requests, 13 changes proposed by the team based on results from UT2 and majority of them are UX/UI changes. We would like to list 3 interesting/impactful 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
11 Dashy, Sponsor Mobile Responsiveness 2 2 High, Allow people to use it on the go 4, High Accept change request and team discuss the most appropriate iteration to implement the change request Closed
25 FaBot, Team Ravenous Input format for date/time too rigid. Require advanced NLP. 2 2 High, This will improve user experience by giving users more options to type 4, High Accept change request and team discuss the most appropriate iteration to implement the change request Closed
36 EvBot, Team Ravenous EvBot will automatically prompt user to link event the moment it detected user has created an event 2 2 High, It will guide the users in using EvBot to link events 4, High Accept change request and team discuss the most appropriate iteration to implement the change request Closed

Technical Complexity

Asynchronous Nature of NodeJS


SetTimeout
To stop the asynchronous calls initially, setTimeout was used. This was used via trial and error basis to see how long a previous request call would take, and run the next block of codes after the elapsed time.

This is easy to implement however, this comes with many disadvantages. For one, this is a highly unreliable method. Although a request call to Graph/Messenger API would usually take less than second, there could be instances where FB’s server slow down and exceed a second. This would cause our app to crash since there would still be many variables not instantiated yet. This method is essentially hardcoding.

Secondly, it causes the code to be very hard to read for other developers. With each setTimeout, the next block of codes has to be indented in. With the nature of our code, we will be affected by asynchronous calls many times, causing our codes to be unnecessarily heavily indented to the right.

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Callbacks
Callback was a better method than hardcoding timeouts. This guarantees a request call is completed before proceeding to the next block of codes for most cases. Callbacks can solve most problems but not all. For example, getting multiple values from request calls first, before proceeding and making multiple request calls in order.

Another drawback to using callbacks, is that the indentation problem is not solved. With every callback function, the next block of code is indented, making it difficult for other developers to read.

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Recursion
To make multiple calls in order, Promises was also explored but it did not work. This could also be due to the lack of understanding the Promise concept at that period. Since timeouts, callbacks and promises weren’t working, we explored the use of Recursion to send messages in guaranteed order to users.

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Promises
Promises required some reading up and researching before actual implementation as this is a new asynchronous concept with little examples online as compared to callbacks and setTimeout. Promises solves all of our problems. It guarantees the request is done before moving on, it can make multiple request calls and storing the values synchronously and lastly, do all these while maintaining clean code.

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This is a really clean way of sending multiple messages and performing other calls within the function. Imagine using callbacks or timeouts, after each sendMessage and delay function, all subsequent codes has to be indented. For the example, the code would have had to be indented more than 10 times!

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We used Promise.all to collect all contents of the resolved body from the promise methods. In the above example, a request is made to Graph API to retrieve the Event Image URL for a list of Events. Thereafter, we use a Promise.all to retrieve all the URLs obtained previously. This is done synchronously.

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Async/Await – Node Version >= 7.6.0
Async/await has recently been shipped with official support in Node 7.6 (released in early 2017). It is built on top of promises and needs to be used in conjunction with them. While Promises keeps code cleaner in comparison to using callbacks, async/await is even more concise.

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In this example, we used the await keyword before calling asynchronous functions (that must return promises). If this were written with standard promises, there would need to be a chain of then blocks and return statements. Async/await makes code look synchronous and improves readability significantly.

ChatBot Sessions

Messenger platform, the platform the team is developing the bots on, does not support sessions out of the box.
E.g.
Bot: Can I take your order?
User: I want a Hamburger.
Bot: What? Why?

Sessions in our chatbots is crucial as our use case requires many user inputs. Even so, user input alone is not enough. Our chatbots will have to take in multiple user inputs in order, and perform validation on those user inputs. When the inputs are invalid, the bot needs to recognize and re-prompt the user for a valid input.

To overcome this problem, we implemented our own ‘sessions framework’. Firstly, we made use of a database to store a user’s session.

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We also implemented a set of codes in a session.js file to manipulate and access the session variables. The way the code works, is that it’ll query the database with every user call to the bot to check for any existing session (e.g. bot is in the middle of prompting Booking Date). If an existing session exists, it needs to listen to the relevant call. E.g. If bot is prompting for a booking date, a bot must expect a booking date, and not a booking time.

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The advantage of a self-implemented session framework, is that we can control validations tightly. When an invalid input is entered, the bot will recognize it, and immediately prompt for a valid input.

However, there are disadvantages to it as well. Firstly, with every user message to the bot, a query to the DB is made to check for any existing session. Even when a user is ‘chatting’ with the bot saying ‘Hello!’, a DB query is made. This will slow the DB down significantly when multiple users are using the bot at the same time, or when a user spams the bot with messages. This caused numerous bugs when the bot did not reply on the first input. When the user enters his input the second time, the bot responds.

Secondly, to have instant validation, users must input in their date and time in a specific format. Any deviations from the format will render it an invalid input. The rigidity was not liked by most users.

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To overcome the UX and performance issue, we made use of an external API (apiai) on top of our codes. We used apiai to parse a user’s message into intents and entities. For example, 11/12/2017, 11-12-17 and even 11 Dec 2017 are all recognized as date formats. This is the same for time periods.

However, this is lacking in terms of validation. If a user enters a date that is before today e.g. 11/01/2001, APIAI will accept as it is still a ‘valid’ date to them. For the bot’s use case, this date is invalid as there is no way a user can book a facility before today. Similarly, if a user types a booking time of 3pm to 1pm, it should be invalid.

To overcome this, we added our set of validation codes.

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Another challenge we faced while using apiai is to allow the user to only fill up the invalid fields e.g. User only needs to fill up booking date instead of booking date + booking time + booking room again in an event of an initial invalid input.

This was difficult as apiai does not allow us to reset the session context on the fly as this is tightly controlled by them. For example, as soon as a booking date is invalid, we tried to send a POST request to apiai to reset the context, causing it to re-prompt user for a booking date. But apiai rejected the call, and will continue asking for the remaining prompts.

A workaround we found, is to manually store all invalid input a user has. With these errors, we’ll make a new call to the API, leaving out those errors. Once these fields are left out, APIAI will only prompt those fields.

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Finally, since session is not supported, it was difficult to pass variables around in our code. We were used to storing variables in a session, and retrieving the variables from the session in another file. Since this was not supported, we coded out a custom function such that we were able to pass variables around using the messenger platform’s Postback/payload system, following the URL query parameters format.

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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

Deployment

The deployment links are as follow:

We have 2 versions of Dashy for different purposes. Staging refers to application in the development phase and production refers to applications are that production ready. Only these two 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 2 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 results of UT1 can be found here
The results of UT2 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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