Chatbot Testing: Deeper Insights to Framework, Tools and Techniques

Over the years, the dynamics of business marketing and implementing technology to drive better customer experience have transformed significantly. Chatbots are one such fine example of the same.   

Almost every website you visit these days provides you with virtual assistance with chatbots. More importantly, chatbots help business owners to manage and expand their business services as well as CRM practices.   

The futuristic benefits of chatbots have captured the attention of many global organizations.  These organizations are actually trying to drive the complete potential of this technology to attain their business goals.  Additionally, chatbots help you to enhance your marketing plans while offering extensive organizational benefits if implemented in the right way.   

Nevertheless, it's not only the deployment of your chatbots in the right manner that necessarily helps achieve your business goals.  The perfect chatbot testing strategy also helps you drive the marketing agenda.    

Whether you are a newbie to chatbot testing, or you are already familiar with its basic concepts, this guide will help you gain insights on everything beginning from testing techniques to frameworks, tools, and more.  

Let’s begin.  

Understanding the Testing Frameworks  

When we talk about chatbot testing procedures, most of the time, they are some points of standardization. Since it can get challenging to meet communication-related objectives, spending time on test cases helps you the faster launch of chatbots. The objective of this test strategy is to work on the most anticipated test practices. Thus, the testing framework for a chatbot is broadly categorized into three main divisions:  

• Expected Scenarios  

• Possible Scenarios  

• Almost Impossible Scenarios  

Usually, these test cases are charted to sigma distances where testing for almost impossible use cases is completed to achieve the 3-sigma distance or 99 percent confidence interval for chatbot performance. Any testing procedures implemented beyond this stage often involve very high investment and are usually done to attain some endless language possibilities.   

A Brief Insight To Various Domains of Chatbot Testing 

When we start working on chatbot testing, it usually involves the below-mentioned types of testing domains:  

• Answering  

• Conversational Flow  

• Error Management  

• Intelligence  

• Intelligence Onboarding  

• NLP Model  

• Navigation  

• Personality  

• Response Time  

• Speed  

• Security  

• Understanding  

However, achieving the finest results from these testing domains needs the right application of the testing techniques which involves agile and developer testing practices. Let’s have a brief detail on them: 

Agile and Regular Testing  

Chatbots are all about agile technology, as it helps to attain the viability required after every loop. Such technology can aid with error-handling functions and prevent bugs with rapid iterations. The initial phase usually involves manual test procedures, which are usually implemented to work on business workflow, while the end stage is usually automated to prevent any waste of time and rapid market launch.   

Developer Testing  

This is a more direct form of testing, which is meant to validate and verify tests by defining answers to user queries in advance. This type of testing is simple and works by checking the precision of answers given by the chatbot against any random questions.   

Chatbot Testing Frameworks  

Defining the operations for chatbots is not an easy task, and therefore, needs analytical ability to overcome any uncertainties with the function. There are numerous frameworks to use for chatbot testing, but before using them it is necessary for testers to understand the purpose and benefits of the available test techniques or frameworks in order to align them with the defined objectives:  

• Advanced Automation Framework: Testing for the end-to-end flow of conversation to identify any chances of self-improvement while understanding natural language

• Domain-specific Testing: To evaluate selected services for business benefits as well as meeting the end-user goals checking for possible use cases  

• KPI Analysis and Real-time Monitoring: To test for chatbot performance by measuring different KPIs like rate of accomplishment, learning rate for AI and ML, Fallback rate, and self-service rate  

• Advanced Security Mechanism: To evaluate the security mechanism for end-to-end encryption, compliance validation, authentication timeout, incorporating of user authentication, intent authorization, channel authentication, and self-destruct message 

Chatbot Testing Tools  

Since chatbot testing needs to offer a pleasing user experience to anyone visiting the website, working on the various domains and practices needs access to the right tools. Here are a few good tools that you may consider for your chatbot testing project: 

Botanalytics  

Botanalytics is an AI-enabled tool that works on conversational analytics while capturing engagement. This tool is made to enhance capability for A/B testing, lead interactions through sentiment analysis, and more.  

Chatbottest  

Chatbottest is a free-to-use tool that comes with 120 questions to assess chatbot experience. This tool works well on all the above-defined domains of chatbot testing.   

Dimon  

Dimon is a tool that you can use to test the conversational flow of your chatbot along with the user experience. Besides, this tool can be used for integrating chatbots with social media platforms like Facebook, Messenger, etc.  

Chatbot Testing Techniques  

Though there are different testing techniques that you can opt for testing chatbots, the selection of each technique depends on the tool you use.  This is an easy way to get all your training data in the model and predict the model. The testing techniques are categorized into two major divisions:  

Industry Standard Cross-Validation   

The MI-based models are usually tested with a statistical approach, which is known as cross-validation. This testing technique works by assessing the model’s capability to predict new data dissimilar from what is used for training. This kind of testing, when done in interactive AI systems, is made to test bots for their scope using example training queries.  

The most basic practices include LOOCV and K-fold method, which is meant to divide data into k groups in which one part is used for testing the model while another part or K-1 is used for training purposes. In short, practice works on iterations for iterations made K times on every split.  

On the other hand, the LOOCV approach is rather a much broader technique that works on possible combinations of original test data to make training and tests. This technique involves fewer computational tests and can be implemented for smaller data sets. This testing should preferably be used before blind testing.  

Blind Testing  

The blind testing technique is usually worked over questions that users are likely to use to get the desired answer. Most of the time, these queries are executed via batch test through a defined model as it helps to mark all queries and ensure that all predictions are correct or not.  

Nevertheless, any method used must be detected for the action steps that take the tester to a particular result. Usually, data visualization is implemented to understand the similarity and differences between different models.   

A confusion matrix can also be implemented by the NLP trainer to detect patterns and retrain the end goals, but not all projects need validation through both types of techniques.  Additionally, the selection of techniques depends on the knowledge, experience, and resources available to the testing service provider firm.  

How Can You Create a Perfect Test Set Without Current Data?  

The testing of interactive AI and implementation entirely depends on the data set used. Therefore, certain rules can be followed by the person developing the test cases to ensure optimal results:  

• Scenario-based test sets reflect on possible scenarios that anyone using the website may encounter. This usually involves intent-based questions.   

• The detailed descriptions offer solutions to the user interacting with bots while incorporating user type, query expression, and difficulty.   

• Align questions and interpretations in a systematic order.  

• Offer well-phrased and value solutions to the corresponding queries.   

• Have the best data source to answer questions asked by users in real-time.  

Common Errors That Must Be Prevented  

To avoid test data, get short on expectations.  Here are a few common errors that must be prevented:  

• Improper preparation on scenarios leading to arbitrary questions uses in testing conversational AI

• Intent variance for similar expressions causing conflict or issues  

• Only including the most general scenarios  

• Lack of clarity in the data set with extensive unrequired content 

Common Chatbot Testing Scenarios That You Must Necessarily Consider  

1. A chatbot should be loaded with the website on which it needs to be implemented.   

2. The chatbot should load clearly when a user lands on the website, either with pop-up or sound.  

3. The chatbot should greet the user based on their time zone.  

4. If an already registered user visits the website, the chatbot should call them by name.   

5. The chatbot should answer queries using the name of the user in between the chat.   

6. If required, the chatbot should ask for the contact details of the user.  

7. It should recognize male and female users well.   

8. The chatbot should identify possible spelling mistakes.  

9. The chatbot should understand currencies and numbers.   

10. The chatbot should verify contact, date, and time for programmed format.   

11. The chatbot should be able to deal with confusion caused due to intricacy.   

12. The chatbot should respond well to pasted text-based queries.   

13. The chatbot should store conversation history and forward the same to the repository if trained to do so.   

14. Chatbots should perform well for simultaneous queries asked from different users at the same time.  

The Crux  

In conclusion, it all comes down to testing conversational AI for the desired features, which can be enhanced through consistent efforts and the proper use of technology. More importantly, chatbot testing involves some critical characteristics of the chatbot lifecycle that can only be attained with the exemplary implementation of the above-mentioned chatbot testing techniques and frameworks with the right tools and the other best practices to prevent error and ensure the correct functioning.   

In a nutshell, it is extremely important that a chatbot must be designed to offer maximum interactiveness on domain-specific tests that are run through analysis of every minute test results. This practice will not only help you to handle your user queries well but create bots that are smart enough to bring your business conversions.   

Thus, no matter if you go for manual checking or use some advanced automation test tools to evaluate your bots, creating a bot that can handle small talks, understand matching intents, and offer precise navigation to users with well-defined fallback can be the little key to your marketing, sales, and customer service strategy.