Nikolett Török at Cyara asks if you have a playbook for testing conversational AI.
Despite their apparent simplicity to the user, chatbots are backed by very complex systems to deliver the flawless customer experience every brand is seeking.
A well-designed virtual assistant is available on multiple platforms, can provide a personalized experience, fulfills – or even better – exceeds all functional expectations, and complies with all security and privacy requirements.
In order to cover all of these features, third-party testing platforms are almost always essential. CRMs, speech-to-text, text-to-speech, language models, and NLP engines all generate extra connections and breakpoints where things could go south.
To assure these capabilities, and avoid the fear that your chatbot will break somewhere, requires a comprehensive testing approach.
When Should I Start Testing My Conversational AI?
Testing conversational AI after deployment is like putting the cart before the horse. This common belief is not unique for testing chatbots, but rooted in every software project where continuous testing hasn’t been introduced yet.
There are certain quality gates you should meet before introducing your digital assistant to the public, therefore testing should have already started during the development process!
Is There a Testing Hierarchy for Conversational AI?
You might be familiar with the traditional levels of software testing, where unit and component tests come first, followed by integration, system and acceptance testing.
When it comes to conversational AI, there’s no “one size fit all” practice to follow. Knowing your chatbot, its capabilities and limitations will highly determine the testing strategy you should implement.
Although there’s no unified solution, we’ve created a playbook for you to better understand your chatbot and identify potential pitfalls before they negatively affect your customers and your reputation.
Where to Start?
Throughout the development process of your conversational AI, it is important that each person who is part of the development is kept in lockstep with each other. If you work in silos, you run the risk of adversely affecting the overall performance of the AI.
Regression testing can serve best as a starting point to do a temperature check on your chatbot in progress and is the easiest way to ensure that a recent program or code change has not adversely affected existing features.
In addition to regression testing, NLP testing serves as the next best stepping point on your conversational AI’s quality assurance journey. It is important to note (and may sound redundant) that this test type is only possible if your chatbot is powered by an NLP engine.
It is essential to test and analyze your chatbot training data and to provide guidance and resources that continuously improve your chatbot’s ability to understand as customers pose questions and requests in unexpected ways.
NLP testing helps you to adjust not only the quantity, but also the quality of your training data, and gives you insights that help you make informed decisions about how best to develop and enhance your chatbot’s performance.
What Are the Next Steps?
Although regression and NLP testing are the basis for the functional readiness of your chatbot, there are many circumstances – and sometimes requirements – that you should be aware of and test your chatbot for.
Depending on the types of tests you may have done so far, you may feel confident that your chatbot understands what you’re saying and can respond if it needs to keep in touch with one person at a time.
After deployment, it is reasonable to assume that the chatbot will have to cope with a larger load, but without a robust testing period, you won’t know the maximum parallel users that the chatbot can serve in a timely manner.
Therefore, performance testing becomes a must-have to determine how your conversational AI performs in terms of responsiveness and stability under a particular workload.
You could put all your efforts into achieving the highest possible confidence estimation in NLP, but it’s all for nothing if the chatbot simply gives up and shuts down when overwhelmed by a higher-than-normal volume of users.
Can We Assume Our Conversational AI Will Behave as Expected?
So, we have a smart chatbot that is able to cope with the expected load and give accurate, timely answers, but we still cannot assume that all sub-systems and integrated platforms work seamlessly together.
When one fails, so does the entire product, making the stability of each component vital to the success of your conversational AI.
End-to-end testing is your best friend to simulate what a real user scenario looks like from start to finish, to validate your conversational AI under product-like circumstances, and to replicate live settings.
The combination of these test types together – regression, NLP, and end-to-end – will give you the confidence to run and maintain your conversational AI, while also taking into consideration important factors such as security and GDPR compliance.