According to the ResearchAndMarketers report, the annual growth of the chatbot market will reach 24% and will remain at this level until 2026. The reasons for this are the impact of Covid-19 on business and the global automation of trade and consulting. Now, in the age of digital technologies, the Internet, and AI, many companies are interested in taking their brands to a new level to follow trends. The main task of chatbots is to simplify communication with consumers, automate the delivery of answers to common questions, and optimize the work of managers to unload them and concentrate resources in priority areas of their activity.
ZappleTech specialists have conducted their own research on automatic assistants and want to tell you about the problems of launching them. In this article, we talk about ways to solve difficulties in chatbot automation testing.
Table of Contents
The Perspective of Chatbots: Business for Customers
AI technologies are developing, and new perspectives for using bots in business as autonomous consultants are opening up. With their help, users receive qualified assistance, answers to their questions, and technical support. The most advanced programs can even place orders and organize delivery from warehouses to buyers. If a lot is already known about the benefits of AI, then not everyone knows how they are developed and modified.
Nowadays, businesses and marketing have revised their values and taken a course towards modernizing user experience and communication. Chatbots have become indispensable helpers for brands on specialized resources, social networks, messengers, and specialized apps. Communication with customers has become a priority task for any organization engaged in trade or service provision. However, huge resources, time, and labor of many specialists are required to create and optimize the chatbot. QA departments are responsible for checking the performance and relevance of bots. They, along with their colleagues, use chatbot automation testing tools and other utilities.
Chatbot automation testing: challenges, problems, and solutions
What is a chatbot? In general, it is code that analyzes user requests and produces relevant results. It consists of a stack of technologies: search algorithms, machine learning, and integration with multiple resources. As for the method of communication, bots can both respond with text and voice. And if there are no significant problems when writing the body of the program, they appear in large quantities during testing.
Back-end and front-end chatbot testing: goals and means
Do you know how testing chatbots is provided? The ZappleTech QA team works with them on a regular basis! We will tell you how digital assistants are tested during development and after release.
The back-end is checked by special utilities, universal or written for a specific product. They are also called “chatbot automation testing tools”. They run pre-written user scripts that mimic the actions of real people, checking the relevance of the output by bots. Tracking and fixing bugs, programmers optimize the code and bring it into a presentable and, most importantly, completely functional form.
Sometimes, there is a hitch with the Front-end because some chatbot automation testing tools do not work with visual interfaces. Usually, QA teams conduct manual checks to verify that the program is working. But it is a routine and time-consuming process, so various automation methods are increasingly used to optimize this activity and track errors more effectively.
Testing methods: chatbot automation testing and manual testing
As a rule, different approaches to testing chatbots give better results in a bundle than separately. Frankly speaking, this statement is true for any testing of IT products. The point is that you can catch bugs manually or automatically in Front-end and Back-end parts. But with manual testing, some errors may not be detected, and with automated testing, there is also a chance to miss small but actual problems.
Manual checking is usually applied at later development stages or at the final release when the code and the interface are prepared and debugged. Testing is done on various devices and scenarios, which accurately simulate the real actions of clients.
Automated tests are often used for bug tracking in helper code in early sprints. This approach minimizes problems directly in the back-end and helps to optimize the software body.
Chatbot automation testing: what is tested by expertsDepending on the business area in which bots are used, the pattern of user interaction with them is different. Therefore, it is required to figure out what the assistant should do and what results to provide upon request. To do this, let’s consider the classic areas:
- Medicine: the bot answers key questions, makes appointments with specialists, and advises on treatment options and procedures.
- Communication: the program analyzes requests, compiles responses, provides information on tariffs and instructions for connecting/changing, etc., solves technical issues, and sends requests to support.
- Media: helps in navigation, receives materials, and analyzes information according to specified algorithms.
- Trade: generates orders, receives requests from users, compares products, and answers basic questions.
- Services: generates prices, templates, accepts applications for processing, and directs clients to qualified specialists.
- Finance: advises on the terms of loans, analyzes credit histories, and even independently makes decisions on the issuance of loans.
Here are the most typical scenarios, but even they lead to some challenges when testing.Universal scripts and functions for chatbot automation testing Let’s start with the trivial: checking the functionality of each critical parameter of the bot. Standard scripts are used almost without restrictions in any chatbot automation testing tools, which reduces the time for writing and debugging them. Many QA specialists such as the ZappleTech team use a universal methodology. They test:
- Response speed: the lower the delay, the better. As practice shows, if users do not receive an answer within 5 seconds, they are 90% likely to leave the resource.
- Relevance: the bot should analyze queries in any word form and give the most accurate result. For this, developers even create special dictionaries that contain all language “perversions”, including transliteration and morphologisms.
- Correction: even if the AI does not understand the issue, it must analyze it and offer the client an authentic option.
- Personalization: bots usually use voice or text to communicate, but there are combined options when it is essential to achieve resonance between the style of the graphic response and the tone of the sound.
- Structuring: chatbots do not always dialogue with users, offering a logical control menu with pre-generated templates. Tests identify inactive items or navigation bugs.
- Learning: if the program uses AI, it memorizes questions and communication styles and learns to produce appropriate results.
- Cross-platform: The chatbot should work the same for everyone regardless of the client’s device: PC, tablet, or smartphone. If it is a web solution, its adaptation should not be complicated, but optimization is necessary for the installed apps.