LTS QA

Smartphone applications are now capable of acting as sources of entertainment (gaming, music, movies), social media updates and even personal management tool. This means mobile apps are expected to perform much more complicated tasks; leading to focus on several areas in mobile application testing. With this trend, mobile application testing tools are also getting more and more diverse in scope.

Therefore, it is crucial to understand the strengths and weaknesses of each of these tools in order to choose the suitable one for specific tasks.

 

Appium

 

 

 

 

 

 

Appium is an open source testing tool for assessing Android and iOS applications. Developers can test mobile applications, web mobile, and hybrid applications by using this software.

To run the test, Appium uses the WebDriver interface which supports C#, Java, Ruby and many other languages that belong to the WebDriver library. The tester is also able to check initial applications written with the Android and iOS SDKs, mobile web apps, and hybrid apps that contain web views. As a cross-platform tool, it allows developers to reuse the source code between Android and iOS.

 

Robotium

 

 

 

 

 

 

Robotium is an open source tool that allows testing Android applications of all versions; it supports the testing of native and hybrid applications. It uses JavaScript to prepare and execute test scripts. Therefore, Robotium is really popular in the case of automated black box testing for Android applications.

Moreover, it automates many of Android’s operations and creates solid test cases in a minimum of time.

 

Special Features

Multiple Android activities can be handled in parallel.

Robotium can create powerful test scripts in minimal time, without having a deep knowledge of the project.

You can even run test cases on pre-installed applications.

 

Espresso

 

 

 

 

 

 

Espresso is one of the most popular mobile testing frameworks. Created by Google and integrated with Android Studio, this mobile application testing tool is familiar with anyone who develops native Android applications. Like TestComplete, this framework has several options for test script generation, but with Espresso, you can create Android UI tests only.

 

Special Features

A platform-specific solution

Supports all Android instrumentation

Supports manual creation of tests using Kotlin and Java

Has a simple and flexible API

Espresso UI tests can be executed on emulators as well as real devices

 

MonkeyTalk

 

 

 

 

 

 

Next, MonkeyTalk automatically tests the functionality of Android and iOS applications.

Even non-technical people can run tests on this application because it requires no in-depth knowledge of programming and scripting. The scripts of MonkeyTalk are easy to understand, therefore, tester can also generate XML and HTML reports. Besides, it takes screenshots when the failure occurs. In addition, MonkeyTalk supports emulators, network devices and tethered.

 

EarlGrey

 

 

 

 

 

 

EarlGrey is a native iOS UI automation test framework that enables developers to write clear and concise tests, developed and maintained by Google.

With this framework, testers have access to advanced synchronization features. For example, EarlGrey automatically synchronizes with the UI, network requests, and various queues; while still allows the developer to manually implement customized timings.

 

Special Features

Synchronization: From run to run, EarlGrey 2.0 ensures that you will get the same result in your tests, by making sure that the application is idle. These tasks are executed by automatically tracking UI changes, network requests, and various queues. In addition, EarlGrey 2.0 also allows you to manually implement custom timings.

White-box: EarlGrey 2.0 allows you to query the application under test from your tests.

 

Conclusion

Test automation is a complex process, and its adoption requires all the team members to put in a great deal of effort and time. The success of automated tests, however, mainly depend on the mobile testing tools you choose.

While looking for the right tool or framework for writing test scripts, pay attention to its features. Be sure to pick a reliable solution that allows different options for test creation, supports multiple scripting languages and mobile platforms.

We gladly announce that, on April 3rd, 2020, the first offshore branch of LQA/LTS named Lotus Japan (LJP) was officially established in Japan; after much expectation of both Lotusians and Japanese clients. The new subsidiary marks an important milestone in LQA’s international expansion.

Mighty ambition in Japan

The presence of LQA/LTS in Japan has been part of our strategy since the first day; as this is one of our main markets. Thus, opening a Japanese branch is our effort to respond to the rising demand on Testing and Annotation service in Japan.

Also, we want to have more chance to understand clients better by meeting directly with them instead of pure online communication.

Lots of hope and expectation

LJP is in Kanagawa prefecture – an ideal place for foreign companies in Japan. Last November, in a business event between Vietnam and Kanagawa, Mr. Yuji Kuroiwa (黒岩 祐治), the Governor of Kanagawa Prefecture, expressed his delightment to LQA’s existence in Kanagawa and the Government’s willing to help LQA/LTS for more years of growth in Japan.

This establishment could not have been successful if it were not for the help of the “Select Kanagawa Next” program and JETRO (Japan External Trade Organization); giving us infrastructural and legal support.

CONTACT:

Meet us at 2F, Industry & Trade Center, 2 Yamashitacho, Naka Ward, Yokohama, Kanagawa 231-0023, Japan or contact us here.

LJP will provide both Testing Services and AI Data Annotation Services respectively.

This is the first video of the series 5 Essentials of AI Training Data Labeling work. Ngoc will talk about data quality and its determinants.

You can watch our video here, or read the transcription below. Turn on subtitles for English, Japanese, Korean and Vietnamese

Hello everyone, my name is Bich Ngoc from Sales Department of Lotus Quality Assurance. You can also call me Hachi.

Welcome to LQA channel. Our channel is aimed at sharing information about testing and data annotation for AI development. If you want to see more helpful videos from our channel, please like and subcribe to our channel.

You are…

• Dealing with massive amounts of data you want to use for machine learning?
• Doing most of the work in-house but now you want your team to focus on more strategic initiative?
• Thinking about outsourcing the data annotation work but still have a lot of concerns?

These video series are totally for you.

With 5 videos in the series, we will take you through the essential elements of successfully outsourcing this vital but time consuming work.

Our sharing is not only from the perspective of a data labelling service provider but also a quality assurance company. So I hope you guys will find it fresh and helpful.

• Data Quality
• Scale – What happens when my data labeling volume increases
• Tools – Do I need a tooling platform for data labeling
• Cost
• Security – How will my data be protected

Today I will introduce you to one aspect you have to consider when you prepare a data set for your AI: DATA QUALITY.

What is Data Quality?

First of all, let’s get to know what Data Quality is.

Simply put, data quality is an assessment whether the given data is fit for purpose.

Why is there even a question of quality when it comes to data for AI?

Isn’t having access to huge amounts of data enough?

The answer is no.

Not every kind of data, and not every data source, is useful or of sufficiently high quality for the machine learning algorithms that power artificial intelligence development – no matter the ultimate purpose of that AI application.

To be more specific, the quality of data is determined by accuracy, consistency, completeness, timeliness and integrity.

• Accuracy: It measures how reliable a dataset is by comparing it against a known, trustworthy reference data set.
• Consistency: Data is consistent when the same data located in different storage areas can be considered equivalent.
• Completeness: the data should not have missing values or miss data records.
• Timeliness: the data should be up to date.
• Integrity: High-integrity data conforms to the syntax (format, type, range) of its definition provided by e.g. a data model

Why is data quality important?

For example, if you train a computer vision system for autonomous vehicles with images of mislabelled road lane lines, the results could be disastrous.

In order to develop accurate algorithms, you will need high-quality training data labelled by skilled annotators.

3 Workforce Traits that Affect Quality in Data Labelling

In our years of experience providing managed data labelling teams for start-up to enterprise companies, we’ve learned three workforce traits affect data labelling quality for machine learning projects: knowledge and context, agility and communication.

Knowledge and context

Firstly, for highest quality data, labelers should know key details about the industry you serve and how their work relates to the problem you are solving.
For example, people labeling tomato images will pay more attention to the size, color and the condition of each tomato if they know the data they are labeling will be used to develop AI system supporting tomato harvest.

Agility

Secondly, your data labeling team should have the flexibility to incorporate changes that adjust to your end users’ needs, changes in your product, or the addition of new products.

A flexible data labeling team can react to changes in data volume, task complexity, and task duration.

Communication

Last but not least, you need data labelers to respond quickly and make changes in your workflow, based on what you’re learning in the model testing and validation phase.

To do that kind of agile work, you need direct communication with your labeling team.

To conclude, high-quality training data is necessary for a successful AI initiative.

Before you begin to launch your AI initiative, pay attention to your data quality and develop data quality assurance practices to realize the best return on your investment.

You can watch our next video on Scaling Data Annotation, or other videos in the series.

Also, try out our series on Data Annotation Tools and visit our Youtube Channel

Interested in our Annotation service?

Book a meeting with us now!

Agriculture is one of the oldest and most significant professions in the world. As the population continues to grow and land becomes scarce, people needed to get more efficient farming practices.

AI (Artificial Intelligence) is now one of the best solution for the problem of the sector, helping farmers to organize farm data, plant healthier crops, monitor growth, and supports a wide range of farming-related tasks. This could bring about a significant shift in how our food is produced respectively.

AI helps organize agriculture data

First, farmers can get data such as temperature, soil conditions or water usage from their farm to better make their decisions with the support of AI.

There is a term, “precision agriculture”, which means technology (including AI) being applied to get better crop outcome. For example, through the study of historical crop data and external sources, AI systems can give exact recommendations of what crops to plant, how much irrigation to have, how much fertilizer is needed etc.

In terms of risk management, they can detect diseases in plants, insects, and poor plant nutrition. AI-powered sensors can detect and decide which herbicides to apply within the right buffer zone. This helps to minimize amount of herbicides and residue that remains in food.

Application of Computer Vision in Farming

Nowadays, farmers can get a better “view” of their crops from above thanks to drones powered with algorithms. This allows them to identify troubles and potential improvements as soon as possible.

According to several reports, a few large industrial farms tried applying those previously mentioned Computer Vision to find and confiscate sicks pigs at the outset of an African swine fever epidemic which adversely influenced Chinese economy. Hence, drones give more potential savings in time within a larger area than humans.

Forecasting Weather data

Helping farmers to remain updated with the data related to weather forecasting is an efficient application of AI. Then, these predicted data help farmers increase yields and profits without risking the crop. After that, analysis of those data could help the farmer to take the precaution by understanding and learning with AI. By implementing such practice helps to make smart decisions on time.

Monitoring Crop and Soil Health

In an advanced way, utilizing AI helps farmers to monitor nutrient deficiencies in the soil and identifies possible defects. Using image recognition, AI identifies possible defects through images captured by the camera. With the help of Al, flora patterns in agriculture can now be . Such AI-enabled applications are supportive in understanding plant pests, soil defects and diseases.

Indoor Farming

There is an evolutionary trend that is indoor farming which means a method of growing crops or plants, usually on a large scale, entirely indoors. This method of farming often implements growing methods such as hydroponics and utilizes artificial lights to provide plants with the nutrients and light levels required for growth. AI-powered indoor farming is creating a future generation of farmers now. Last year, for example, 80 Acres Farms, who produced indoor growing movement, launched the first fully-automated indoor growing manufacturer in the world. The enterprise’s AI-driven technologies control all critical stages of the commercial development.

In conclusion, with the application of AI in agriculture, farmers around the world could run more efficiently, enabling farms of all sizes to operate and function with keeping the world fed. The future of AI in agriculture is way ahead in offering radical transformation with advanced approaches.

As mentioned throughout the article, good input data is vital in developing meaningful AI algorithms.

You can refer to AI labeling, the foundation for AI here or join our webinar this July below: