87.5 percent of searches made worldwide are through Google in which 20 percent is through voice search. Speak into a digital apparatus, and presto: Play a melody song, find the nearest Italian restaurant, call your mom or book an appointment with a spa.
According to a new report by a market research firm called Futuresource Consulting, Siri is the most popular virtual assistant across the world with a 35 percent market share. Microsoft’s Cortana is, surprisingly, at the second spot with a 22 percent share. Google Assistant and Amazon Alexa account for 9 percent and 4 percent of the market respectively.
Voice-based device/ App
Voice devices are smart devices designed specifically for getting tasks done through voice conversations. These devices are always awake and listening. There are two such devices that are ruling the market those are Google Home and Amazon Echo. These devices can be used for setting up alarm, reminders, searching the web, and even ordering food.
Layers in Testing a Voice-based device/ App
This type of test is targeted to voice-based app developers. They need to do unit testing to ensure the code is working correctly in isolation. As you perform unit testing while coding, you need it to be fast, so it doesn’t interrupt your coding pace. Unit testing is focused on making sure your code and logic are correct so there is no need to hit the cloud (where most voice apps backend lives), or call real external services. It is important that the unit testing tool you choose supports mocks and preferably runs locally.
This type of test is targeted to QA teams. It ensures the entire system is working correctly — AI (ASR + NLU), code, and external services. The tests should be comprehensive and if possible easy to write, read and maintain. As closely as possible, we want to imitate what real users do in their everyday interactions with voice apps.
Continuous Testing (Monitoring)
This type of test is targeted to Ops people and is to ensure that a service, once deployed, works flawlessly. This kind of testing verifies your voice-based app at a regular interval. It should be easy to set up and the results should be delivered immediately when the voice-based app has stopped behaving as expected.
Usability Performance Testing
Usability Performance Testing makes sure that the AI components of the app are working correctly. It is targeted to Product Managers and developers. The main goal is to identify issues with the speech recognition and NLU behavior of the assistant and your app. It consists of comprehensive testing of the interaction model, creating a baseline set of results. These results, in turn, are the basis for making improvements to the interaction model and the code of the skill – once revisions are completed, additional tests are run to ensure everything is working as expected.
Challenges faced in Voice-based device/ App
High volume of datasets: When it comes to operating a voice-first device like Alexa, all it involves is the user saying the “wake up” word, followed by the command or instruction and the device/application responding with the appropriate action. Though on the outside this may look like a simple function, if we go deeper we can see many layers of complexities.
Creating dataset based on different domains: Each domain has a different set of core questions or needs that the services revolve around. Manually generating questions for domains may be a very tedious task at the same time does not guarantee coverage of all relevant questions and answers. It will also take a huge amount of time to understand what challenges or questions a customer would want to ask.
Language and accents: It is obvious that people speak different languages in different regions. Therefore, voice assistants need to be able to understand and answer queries in various languages. So how will we test skills in various languages? This is another challenge when it comes to selling your devices across the world. The most important factor here is personalizing the experience. Testers will not know all the languages in which the device needs to be tested.
Different age groups: When we try to sell a product out in the market, we want to cater to a large audience around the world. The audience can be of different age groups. Voice first based devices are not built for specific age groups. When you are catering to an audience of various age groups, you also want to ensure that it provides a great user experience to all customers.
How to overcome these challenges?
The key ingredient that can help testers overcome these challenges is test automation. When you have large volumes of datasets, it is very difficult to impose manual testing. The solution here is the automation in voice-first testing.
Crowd4Test has a dedicated team for testing these kinds of voice-based devices/apps which supports automation of a variety of smart devices like smart speakers, headphones, smart home appliances, and home automation devices among many others.
Crowd tester will be able to provide multilingual by automatically translating questions into various languages, or according to age groups and accents.
Automated Voice Testing (AVT)
This approach makes use of Speech Recognition Engine (SRE) for automatic control of the voice-first devices just like any human being would.
The Key Benefits
This approach of testing is platform-agnostic and device-agnostic as tests are automated at voice level. Hence, you can execute automated tests against any voice app running over any device.
AVT can be applied over these use cases:
- Alexa apps testing (Amazon Echo device).
- Google Home apps testing (Google Home device).
- Voice-enabled web apps and websites.
- Testing virtual assistants such as Siri, OK Google on Android, Cortana, etc.
Voice-based apps are proving that the interaction between humans and machines can be frictionless. This is indeed a new method for the brands and businesses for engaging better with the consumers by rendering an easy, familiar, and fast voice experience.
Explore the Voice-based App Testing with Crowd4Test