How to test

Artificial Intelligence Markup Language Applications (AIML)

In the age of AIML, most companies’ websites and applications have a chatbot channel of their own. They’re relied upon to further the reach of businesses and help interact with their leads, customers and clients quickly and in a more conversational way. While there are multiple scenarios where chatbots are ideal, it should be noted that chatbots do have their shortcomings; primarily, the inability to provide quick responses to their customers. Congestion, unavailability of sufficient staff, and odd query times are some reasons for the lack of a speedy response. Whatever the reason, in the end, the business is affected.

Chatbots, right now, are at the fundamental training stage. Their answers are dependent on the old/current discussions, client questions, or the training data. They can respond to vague inquiries only after set answers have already been prepared. It should be noted that testing can improve all of this. Vee Technologies has proven the capability of ensuring the chatbot gives the necessary expected answers to customer queries with the proper testing.

The major applications in the industry where this has been effective include:

AIML - Industry

The strategies for chatbot testing include


Use cases

The use cases are to be identified initially to:

  • Analyze the questions that are to be raised
  • Check how the chatbot is reacting to each and every inquiry
  • Check the performance of the chatbot when an increasing number of solicitations are made
  • Gather the necessities that are to be tested
  • Locate the key performance indicators (KPI) and number of steps to complete the solicitation/number of clients

Functional Testing

When the necessities are determined, testers must comprehend the basic design and innovation the chatbot will use for each utilization case. From a technology vantage point, the following are the important aspects for testing:

  • Verification of chabot conversational capability
  • Confirm level of insight the client anticipates
  • Ensuring entered data is plainly distinguished and reported

Example:

  • Self-service rates (i.e., the question raised by a user can be answered up to what extreme without human help)
  • Mean client rating
  • Deals transformation rate (i.e., how the chatbot can include an online conversation into the dealing)

Testable requirements

Fundamentally, chatbots are built on natural language processing (NLP), a methodology for PCs to inspect and glean significance from human language. However, their development model may differ. First, one must understand the fundamental structural design and technology that the chatbot will use for each use case.

Test scenarios

The scenarios to be covered include:

  • Incorporating discussion and voice testing (i.e., the ability to recognize speech patterns and interpret non-verbal cues)
  • Planning with variety of similar inputs
  • Handling various directions in a solicitation
  • Engaging discussions with foundation clamours, various styles, and confinement
  • Testing to measure and approve the capacity of the chatbot to help the client
  • Checking if its ability to deal with mistakes is functioning
  • Conducting omnichannel similarity tests, ensuring the indistinguishable look, understanding, and reactions are required (if the chatbot is anticipated to be utilized over numerous channels.)

Non-functional

Situating execution testing, (i.e., the speed at which the chatbot reacts and security testing including verification, approval, encryption of discussions, and adherence to consistency are critical).

Security testing

Answers to the questions can be acquired from the voice bot. For example, checking if OTP is coordinated with the enrolled versatile number or performing a face check with already-put pictures in the database.

Facial recognition

  • Auto-Proctoring - Monitoring the user's facial emotions/expressions using the camera app. This process provides some valid data that is critical to understand what is being said or written.
  • Emotion capturing while the examiner attends the exam, based on the captured images.

Chatbots are an essential communication tool for any functional business. Although how the chatbot is designed, tested and deployed will determine the success of the tool and how the customer responds to it.

How to test Artificial Intelligence Markup Language Applications (AIML)

In these unprecedented times, Vee Technologies is committed to doing our part to help those in need. With the ongoing spread of COVID-19 (coronavirus) and the incredible speed at which things are changing around us, organizations are continually adapting their operations and business structure. Vee Technologies has the experience and capability to help your organization through this difficult time. We've expanded our continuity plan and infrastructure to accommodate the current business needs. Please do not hesitate to contact us when your need arises, our implementation team can have you up and running quickly and efficiently.

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