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4 Key Things to Consider When Testing a Mobile Application

4 Key Things to Consider When Testing a Mobile Application. Behaviour During Different Operations, Testing Across Different Configurations

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4 Key Things to Consider When Testing a Mobile Application

As brands realize the advantages, mobile applications afford them, each of them wants to have the best mobile application. Every tremendous mobile application undergoes a series of tests to ensure that it performs as expected and has an acceptable ROI, and is an essential business tool.

Without proper testing, the quality of apps we have on the market would decline, with uncaught bugs, errors, and anomalies becoming a lot more common. To avoid this, mobile app developers should consider a few key things to ensure they catch as many bugs and errors as possible and deliver a great product.

1. Behaviour During Different Operations

Behaviour During Different Operations Testing a Mobile Application

Operations cover everything that happens to a mobile device and how an app behaves. One of the most critical functions is connectivity. Mobile app testing must cover what happens if a mobile or Wi-Fi network goes down. Other cases include when the phone is in aeroplane mode, or the phone switched from 5G to 4G or even 3G.

The other operation to think about is interruptions. How does the app behave when there are other app notifications, system notifications, incoming calls and texts, or a forced system update?

2. Testing Across Different Configurations

Although a developer should test their mobile application on the platform they are developing for, be it Android or iOS, there is a lot more to consider when it comes to mobile devices.

The most important considerations here include the version of a user’s operating system, different hardware configurations, screen dimensions, and a lot more. Each of these different layers can negatively or positively impact the usability, performance, and user experience provided by an app.

Because of the plethora of mobile devices, mainly Android, being released every year, there is no comprehensive mobile testing strategy that can cover all these devices.

Instead, developers can use mobile app testing services and test processes that are either specific to app testing or general quality assurance to catch any issues that may have arisen in the development process.

3. Testing on Physical or Emulated Devices

Whether it is better to test on real devices or emulators has been debated for a long time. It is an important question to consider when trying your mobile app.

Using emulators is cheaper than buying a suite of phones, but it is limited. This is because emulators cannot test functions such as camera features, geolocation features, biometric scanners, and more. Where possible, developers should opt for real devices to help them carry out more thorough tests.

4. Automated or Manual Testing?

Automated tests maximize effectiveness and efficiency. However, their use will depend on the type of app being tested and the objectives of the tests. On the other hand, manual testing is not a viable option, especially if a developer would like to carry out many different types of tests.

Testing a mobile app is a critical step in ensuring it functions as it should. Proper testing strategies should be developed, although these strategies will depend on the specific app and how much testing the developer is willing to do.

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How do collect and train data for speech projects?

Data collection is the process of gathering, analyzing, and, measuring accurate data from diverse systems to use for business process decision-making, speech projects, and research.

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How do collect and train data for speech projects

With technology evolution, we are moving towards machine learning systems that can understand what we say. In our daily lives, we all have encountered many virtual assistants like Alexa, Siri, and others. These virtual assistants often help us in tuning the lights of our homes, finding information on the internet, and even starting a video conference. But do you know how it does that?

To produce results, these virtual assistants use natural language processing to understand the user’s intent. Natural Language Processing technology enables virtual assistants to understand user intent and produce outcomes. Basically, these virtual assistants are applications of automatic speech recognition and are also known as speech recognition software. This software uses machine learning and NLP to analyze and convert human speech data into text.

But, attaining maximum efficiency of these software requires the collection of substantial speech and audio datasets. The purpose of collecting these audio datasets is to have enough sample recordings that can be fed into automatic speech recognition (ASR) software.

Furthermore, these datasets can be used against the speakers using unspecified speech recognition models. And to make ASR software work as intended, speech data collection and audio datasets must be conducted for all target demographics, locations, languages, dialects, and accents.

Artificial Intelligence can be as intelligent as the data given to it. Hence, collecting data for feeding the machine learning model is a must to maximize the effect of ASR. Let’s discuss steps in speech data collection for effective automatic speech recognition training.

1. Create a Demographic Matrix

For creating a demographic matrix, the enterprise must consider the following information like language, locations, ages, genders, and accents. Along with these, it is a must to note down a variety of information related to environments like busy streets, waiting rooms, offices, and homes. Enterprises can also consider the devices people are using like mobile phones, headsets, and a desktop.

2. Collect and transcribe speech data

To train the speech recognition model, gather speech samples from real humans and take the help of a human transcriptionist to take notes of long and short utterances by following your key demographic matrix. In this way, human is a vital and essential part of building proper audio datasets and labeled speech and further development of applications.

6 Reasons to Transcribe Audio to Text

3. Build a separate test data

Once the text subscription is completed, it’s time to pair the transcribed test with the corresponding audio data and segment them to include one statement in each. Later on, take the segmented pairs and extract a random 20% of the data to form a set for testing.

4. Train the language model

To maximize the effectiveness of the speech recognition model, you can train the language model by adding general additional text that was not additionally recorded. For example in canceling a subscription, you recorded one statement that ‘I want to cancel my subscription, but you can also add texts like “Can I cancel my subscription” or “I want to unsubscribe”. To make it more effective and catchy you can also add expressions and relevant jargon.

5. Measure and Iterate

The last and important step is to evaluate the output of automatic speech recognition software to benchmark its performance. In the next step take the trained model and measure how well it predicts the test set. In case of any gaps and errors, engage your machine learning model in the loop to yield the desired output. 

Conclusion

From travel, transportation, media, and entertainment, the use of speech recognition software is evident. We all have been using voice assistants like Alexa and Siri to complete some of our routine tasks. To effectively use this speech recognition software requires proper training in the audio datasets and the use of relevant data for the machine learning model.

Proper execution and the right use of data make sure the speech recognition software going to work efficiently and enterprises can scale them for further upgrades and development. As data and speech recognition go hand in hand, make sure you are using data with the right approach.

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