A Guide to Chatbot Testing Framework & Techniques

From social networks, e-commerce platforms to websites, every professional today uses chatbots to expand its business and manage CRM (Customer Retention Management) interactions like a human.   

Due to timely investments and futuristic benefits, many companies leverage chatbots to realize their full potential. Moreover, chatbots prove to be a valuable addition when used correctly to gain benefits within the organization and enhance your marketing plans.   

However, when it comes to achieving business goals, sometimes the deploying of a successful chatbot even doesn’t guarantee whether your targets will be meet thoroughly or not. Thus, in this case, testing a chatbot with your marketing strategy is crucial to ensure that the chatbot has a clear tone and voice to initiate a conversation with users. In addition, you can assure that the chatbot will interact with your users appropriately, or people will be able to understand what the chatbot is all about.   

Besides, there are some factors that require testing in different areas of chatbots. Some of those factors are given below:  

• Understanding: Ensure that you’ve developed the chatbot to increase your leads and sales, which means your aim is to help the chatbot interact more with your targeted customers. At this point, you should never skip Chatbot Testing, especially if it is a matter of your bot understanding. Though, people will be able to resolve their queries if and only if you allow chatbots to become problem-solver. Thus, whenever it comes to checking the understanding of a chatbot, you must test small to larger requests, emojis, idioms, small talks, and all patterns' users use for conversation.   

• Answering: What types of messages does the chatbot send, and how well does it send them? Are they appropriate according to time and situation?  

• Navigation: How easy is it to go through the chatbot conversation. Do you ever feel lost while conversating with the chatbot?  

• Error Management: How well does the chatbot handle all of the errors that are going to happen? Is it capable of eliminating them?  

• Intelligence: Is the chatbot intelligent? Can it remember things?  

• Response Time: Your consumers want quick replies & responses. Thus, to engage your customers through faster responses, it is vital to test the bot as per its requirement.   

The Top Frameworks To Use For Chatbot Testing 

Botium.ai - Botium is a suite of open-source software components that chatbot makers can leverage to take training and support their quality assurance. You may hear that chatbots are useful to drive the industry.  

However, companies are using Botium to drive chatbots because it has the capability to automate your Chatbot Testing and helps your users get an improved customer experience. Besides, Botium Box is also available for free in the market and runs on standard components. If you are ready for testing chatbots leveraging AI-based Planning, you can consider Botium Box because it is easy to install on your server, plus allows you to take the assistance of any cloud provider for serverless installation.  

Discover some game-changing features for  training and testing of chatbots: 

Test Management – Connect your chatbot via Quickstart Wizzard. After that, select test environments and datasets to start testing.   

Predefined Datasets: In Botium Box, you can find Datasets for several chatbot domains and consider them to save your time while writing and designing conversations.   

End-to-End Testing: With Botium, you can test your bot from start to end. Start it from API Level and then move further to the user interface testing to make the chatbot interactive and to execute tests faster than manual testing.   

GDPR and Security: One of the biggest advantages of using Botium as a Chatbot Testing Framework is it help you Identify GDPR and Security issues and make it easy for you to fix them as soon as possible.   

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We are the Best Software Testing Company to make your privacy program completely GDPR Compliant.  

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Chatbottest.com - Chatbottest is another open-source guide that you can use to identify chatbot’s design issues under 7 distinct categories: Personality, Onboarding, Understanding, Answering, Navigation, Error-Management to Intelligence; every single factor of chatbot can easy to test with Chatbottest.com.    

Dimon.co - Dimon is an AI-based platform for chatbot testing and performance monitoring. Additionally, this platform has a set of tools that allow chatbot owners to identify and fix issues while conversating with bots. Similarly, Dimon is suitable for automating bot testing, developing scenarios to create bot functionality and alert users in real-time whenever any problems occur in the chatbot. Besides, Dimon is compatible with various platforms like Skype, Telegram, Facebook Messenger, Kik, and WeChat, plus it offers large community support for small bot builders.   

Zypnos.com - Zypnos is the record and run tool to automate the regression test for your chatbots. One of the main aims of Zypnos is to help you get quality benefits for your end-to-end chatbot, and it is ideal for simplifying the monotonous tasks of chatbot testing. Remember that chatbots are the future of online business. If they are of high quality, you will be able to experience productivity in your business. However, if the chatbot has so many errors, you should consider Zypnos because it is an intelligent automated solution that uses AI and Machine Learning to help you enhance the quality of chatbots.   

What Are Some Techniques to Remember for Testing Chatbots?  

 There are a variety of techniques available to test chatbots that entirely rely on the type of tool being used. The best way is to focus on the whole training data in your model and correctly predict your model.  

On the other hand, the testing techniques are roughly split into two main types:  

Industry Standard Cross-Validation Techniques  

Models that are based on Machine Learning and tested leveraging a statistical approach are called cross-validation. In this testing, the capability to assume new data can vary according to the model uses to evaluate for training purposes. Moreover, this type of interactive AI system suggests that test the bot using queries from the scope of examples utilized for training it.   

Furthermore, standard practices involve LOOCV (Leave-One-Out Cross-Validation) and K-fold. The K-fold method is used to divide the data into K groups, and in this process, one part is used for testing the model and the remaining part (K-1) for training. On the other side, the LOOCV method is a comprehensive method that aids in testing the model by dividing the original sample into testing and training sets. It has a lower computational cost and can be utilized to train small data sets. Before moving to blind testing, one should concentrate on cross-validation techniques as per experts.   

Blind Testing   

Blind testing involves testing data with a list of questions that users might enquire about along with the equivalent exact answer. These queries implement through the model and by using a batch test. During this process, every question is marked to understand whether the model's prediction was correct or not.  

A confusion matrix is also useful for displaying the model's expected objectives so that the NLP trainer may spot patterns and retrain the goals as needed. Now, if you are a developer, you may understand that there is no need to evaluate both kinds of tests as a compulsion. However, it entirely depends on the project’s needs and your knowledge to analyze which factors are essential to test. 

The Crux: Creating A Good Test in the Event of Non-Available Current Data   

All in all, testing of interactive AI and its successful executions depend upon the chosen data set. Therefore, before creating a blind test for AI, the following things you should keep in mind.   

• Well-explained descriptions: Always have a detailed description of an issue because it allows the bot to offer the right solution to the user. Similarly, you should include the following things in your description – type of user, the difficulty face, how users will express their queries.   

• Scenario-based - Consider as many possibilities that users might face while interacting with a conversational AI. It will help you combine intent-based queries with unique replies.   

•  Align interpretations: One should arrange the queries that users ask from the bot in a logical order.  

•  Well-defined answers: Make sure that the queries in the training set have well-defined and valuable solutions.  

• Queries based on ground reality: Always choose the best data source while testing, mainly that involves general questions asked by real users.    

In case, you need to have a pleasing chatbot testing experience, its better leaving your test process to the experts. 

Check out our past resources based on Chatbots & Software Testing: 

• Bots: The New Perimeter and How to Perform Testing on it 

• Software Testing Trends that Will Rule 2021 

• 5 Ways AI will Change Software Testing