Chatbots are AI-based computer programs that simulate human conversations by understanding context and deriving meaning from spoken or written language. Bots interpret the objective of the conversation and provide relevant answers or directions. Chatbots communicate through text as well as voice and are classified according to the industry in which they are used such as banking chatbots, medical chatbots, personal finance chatbots, etc. Chatbots have become the favored medium to strengthen customer support.
Spike in the Demand for Chatbots
Unlike human executives, bots respond to customer queries around the clock, and ever since the emergence of COVID-19 there has been a great spike in the adoption of chatbots by organizations around the world. There is adequate realization now, that customer support cannot solely depend on human resources, and having an intelligent chatbot is absolutely crucial to prevent customer dissatisfaction due to delayed responses.
Why is Chatbot testing essential?
While organizations are charmed to utilize chatbots as the new and always up and running client support channel, it doesn’t seem to enthuse the end-user to the expected extent. According to a recent Forrester report, 54% of customers expect a chatbot to negatively affect their experience. Many of the chatbots fail because they don’t understand the customer’s intent which is due to insufficient training and testing. Training and testing of chatbots include several stages and specific techniques to carefully shepherd it to a stage where it mimics a human conversation flawlessly.
The biggest difficulty of using a chatbot in business is its possible failures. Most generic issues can frustrate customers and affect the brand’s image and business efficiency. Broken scripts that result in malfunctioning, delay in replies, no connection to other business channels, lack of accuracy, rusty navigation, and improper conversation design are some frequently noted problems in poorly designed, inadequately tested chatbots.
Depending on the industry, the process of testing a chatbot varies, but certain principles remain the same. Some of the major considerations for QA experts as they build their testing procedure for chatbots are listed below:
- Start with identifying use cases for your chatbot. Prepare a set of questions and possible answers for each scenario and prioritize them according to their importance. Importance can be measured by evaluating its frequency in real-time usage or by weighing the gravity of the query and the consequence of the chatbot providing an incorrect response.
- Evaluate the conversational capability of the chatbot and the degree of intelligence the customer expects from it. Testable requirements and key performance indicators (KPIs) should be clearly defined for each use case. The data types that the chatbot allows should also be clearly identified and documented. KPIs can be
a. Self-service rates, which is the total number of customer engagements that the chatbot can resolve on its own without the involvement of a customer service executive.
b. Conversion rates, which is the ratio of the business conversions brought in through a chatbot conversation to the total number of customers engaged.
c. Average customer rating.
- Once the testable requirements are clearly listed, understand the fundamental architecture and technology upon which the chatbot has been built. Chatbots are usually built on Natural Language Processing (NLP) which is a way for computers to analyze and derive meaning from written or spoken language. Understanding the underlying architecture is essential for appropriately training the chatbot.
- Training of chatbot should also consider the variations that affect the written or spoken conversation, making it unique. Specific dialects, slangs, abbreviations, conversations with background noises, unique dictions, and handling of multiple instructions in the same request are a few additional scenarios to be tested. Additionally, the ability of the chatbot to handle errors should also be validated. If you wish to use the chatbot across multiple channels, user interfaces, and devices, Omnichannel compatibility testing should also be planned for.
- From a non-functional perspective, testing the speed at which the chatbots respond to each of the customer queries while serving hundreds of other customers simultaneously has to be tested. Testing the security features of the chatbot such as authentication and authorization, the encrypted transmission of messages, and adherence to regulatory compliances are important too.
Features to be checked while testing Chatbots
Conversational flow
As a tester, your primary aim should be to test if your chatbot is able to understand the queries of customers within the context of the conversation and provide appropriate answers within the defined timelines. Whether the chatbot keeps the customer engaged during the processing wait time also needs to be tested. Start with the most basic, frequently asked questions, then move to the critical test scenarios and finally tackle the edge cases.
Since human language is full of nuances like two-sided connotations, humor, irony, sarcasm, and so forth, they should also be featured in the training of the chatbot. Ask the following questions to yourself and see if the answers satisfy your expectations to evaluate the progression of chatbots.
- Does your chatbot comprehend the query within the context of the conversation?
- Does it generally give prompt replies to these queries?
- Are the responses appropriate to the posted queries?
- Are there enough steps before the chatbot provides a solution or are there too many?
- Does your chatbot keep the user engaged during the wait time?
Business-specific queries
When the conversational stream has been approved, the tester should continue to test how the chatbot is addressing business-explicit inquiries. Today, chatbots are utilized predominantly by banks, retailers, emergency clinics, and administrative organizations. Every industry has numerous jargon and subtleties. Such domain-specific inquiries should also be featured in the testing process.
Confusion handling
Confusion may arise if a client enters some articulation with a multifaceted nuance or an obscure word to a chatbot. The chatbot should be educated to provide an amicable reply in such a circumstance, asking the customer to provide his query again or reword his query. The tester’s goal is to check if the chatbot can deal with errors, extraordinary and uncommon situations too.
Speed and Accuracy
Users expect the chatbot to provide accurate and timely responses to their queries which underlines the importance of performance testing. Latency — the time taken by a chatbot to retrieve the response and relay it to the customer should be as low as possible. Another fundamental test is for the exactness of the response. Test designers should calculate the number of times the bot offered accurate responses to differently worded queries of the same meaning.
Format validation
If a chatbot accepts inputs such as email addresses, telephone numbers, and postal codes, it is essential for it to detect the right format for such information before processing it. The chatbot should inform the user to modify the details if the information is found to be invalid. The chatbot should be trained on an exhaustive dataset using which format validation behavior needs to be checked thoroughly.
Interface and Graphical content testing
Chatbots regularly provide to the customer: links to a brand’s site or other resources, cards with inserted data, or pictures of various formats and resolutions. All these elements should be functioning as expected, without any glitches. All accepted formats, apart from text, should also be included in the training to see if they are displayed on the chatbot interface as expected.
Compatibility Testing
Testing whether the chatbot looks and functions the same on all of the above parameters on different gadgets is termed compatibility testing. Test whether the chatbot UI elements and their usability remain consistent on different devices, browsers, and OS versions. Whether the graphics appear undistorted and the UI of the chatbot window doesn’t break while resizing a browser window on a PC screen need to be tested. Texts should remain readable and not flood out of the limits.
Error Handling for Unknown inputs
It is important to investigate the limits of the programmed logic and to make sure the bot returns decent responses to the inputs it doesn’t understand. Exploratory testing is a valuable technique to learn how a chatbot handles non-standard inputs that include:
- Easygoing discussions that are unrelated to the chatbot’s purpose.
- Useless sentences or articulations that usually don’t feature in customer conversations. Language blunders, incorrectly spelled words, variations in spelling in English.
- Befuddling and hostile messages.
API/Integration Testing
As a rule, a chatbot doesn’t work on its own — it becomes an integral part of the organization’s computerized ecosystem. That is, you’ll eventually need to associate it to your official website, social network account, messenger, application, or any other digital medium.
If you use bot-building programming, you are probably going to discover an API that permits interfacing a chatbot to your framework and run API tests on it. If you utilize a custom arrangement, it is vital to run integration tests to check how the bot along with the other framework behaves when associated.
Quality tips to keep in mind while testing chatbots:
- Data validation should take place immediately and be followed by a corresponding message. For invalid information, the message returned should notify the customer about the mistake in his query. If the information is legitimate, a client should see a success message indicating that the bot has accepted the provided information.
- Customers should be asked to review, rate the chatbot experience. Real-time users may find and report a few imperfections that might have been missed during the training/testing phases.
- Before going live, the chatbot should be tested on widely utilized gadgets, programs, and OS versions.
- Chatbots should be formulated with and tested to provide an amicable reply to perplexing questions.
- Test to check if the chatbot avoids getting into cyclic loops.
- A discussion with a chatbot should have a sensible stream of the necessary information, similar to a conversation with a customer support executive.
- Focus on input varieties — different languages, formats, lengths, and combinations of characters.
- Make sure that the chatbot is intelligent enough to remember the details provided by the customer earlier in the same conversation. Never should it ask for a detail that’s been already shared.
- When the chatbot isn’t able to resolve a particular query it should facilitate a smooth transition to a customer support executive and he/she who takes over must have all the information that the customer had provided to the bot.
- Automation of chatbot testing can be done by using another chatbot that’s matured to interact with the chatbot that you are testing.
- Categorize the testing of a chatbot into three levels: possible scenarios expected scenarios and almost impossible scenarios and ensure that the chatbot provides expected replies in a prompt manner for each of these.
- Check if the chatbot aligns with the tone of your audience and the nature of each conversation.
- There should always be the last message flagging indicating the end of the discussion.
- If there is a delay in the chatbot response in excess of five seconds it is bound to cause frustration, and the customer would probably abandon the attempt to get the issue resolved. So make sure the customer is responded to within five seconds and if the wait time is longer, the chatbot should provide constant updates about what’s going on.
- It would be great if a chatbot can identify the customer’s time zone and greet them at the start of the conversation. It’s a small gesture but it goes a long way in giving the chatbot a humanistic touch.
Conclusion
Testing a chatbot is not a simple task as it involves technical understanding of its internal programming, planning, ideation, application of techniques, datasets, prediction of real-time scenarios, and expert thought processes. Furthermore, a chatbot will require constant support and upgrades. After the first release, when the chatbot gets to converse with real-time users, that’s when the real test happens.
Receive feedback from customers constructively and strive to provide better updates. An ongoing and continuous testing process is essential to keep the chatbot relevant, accurate, and future-proof its performance.
