How machine learning in software testing produces superior products

In software development, testing is an indispensable part of the life cycle. Machine learning in software testing will be a vital component for producing quality products.

According to research, global annual revenue from AI software is forecast to grow 1,248%, from $10.1 billion in 2018 to $126.0 billion by 2025. Experts project that AI will contribute nearly $16 trillion to the global economy. Productivity will transform as industries can vastly augment their workforce with AI. With all of this growth, testing software stability will require an evolution.

“Machine learning (ML) and artificial intelligence (AI) are evolving software testing quality assurance as they can identify bugs faster, they can handle large quantities of data, potentially keep development costs lower, help reduce human errors, and can provide predictively forecasting to make better data-driven decisions.”

In this article, we will explore many crucial topics QA testers are experiencing in a progressing era of software testing.

What is machine learning?

Machine learning is the study of pattern recognition and computational learning at scale. Once trained, the machine learning engine can learn without explicitly being told to do so. There are a variety of ways machine learning retains information. According to Hackernoon, the 3 core learning types are:

• Supervised Learning – when the algorithm is given the correct training data to learn.
• Unsupervised Learning  –  when the algorithm is given a ton of data and tests to see what the machine has learned.
• Reinforced Learning  –  is based on the ideals of the rewards system. Reinforcing learning by training the algorithm to be rewarded for “good” behavior and corrected for “bad” behavior.

Practical examples of machine learning

There are many practical examples of ML in software that help solve real-world problems. For example, Airbnb uses machine learning to help categorize listing photos. The core challenge resides in the fact that they have accumulated hundreds of millions of photo listings. And some images can be improperly labeled due to user input error. How do you fix this?

Airbnb’s development team could have tasked a team of users to manually tag images. However, at scale, this was time-consuming and could have taken weeks (or months.) By training a machine learning model, this process took days.

The other significant challenge resided in the machine deciphering what was in the picture. Such as distinguishing electric fireplaces from wall-mounted televisions.

For example, future applications they can deploy can detect features and amenities in a home from photos versus relying on the owner to list them. So if a renter wanted a specific listing with a “gold clawfoot tub” that amenity can be searched for.

Use cases in software testing

In software testing, Autify uses machine learning to automatically detect changes in user interfaces in regression testing.

While running recorded tests, the AI engine learns of every element in the UI. If a step does not return the expected results during testing, the AI engine detects the element and saves the change while completing the test- rather than failing and stopping.

Tests that fail and stop due to frequent UI changes is one of the most frustrating and costly challenges for QA testers in software testing. As they have to spend time figuring out what changed, rewrite test script code, and rerun tests.

Key industries projected with the greatest opportunities from ML and AI

The top sectors that will have the greatest benefit from machine learning and artificial intelligence are in:

• Healthcare Automotive
• Financial Services
• Retail
• Technology
• Manufacturing
• Energy
• Transportation

For instance, in healthcare, medical officials can use data-based diagnosis to support decisions. The barriers though are privacy concerns, protection of sensitive health data, and the complexity of the human body.

In a case study with the Tambua app, physicians in Nairobi, Kenya used machine learning software to assist in more accurate respiratory disease diagnoses. The challenge was in relying on the traditional stethoscope listening for anomalies. The human ear cannot hear all frequencies which can contribute to misdiagnosis.

Developers sought to train algorithms in sound analysis. By developing a large data set of various patient breathing patterns. They could train the machine to learn patterns of “crackles” or “wheezes” to aid in more accurate respiratory diagnoses.

Why is machine learning in software testing so important?

The importance of quality assurance cannot be overemphasized. Furthermore, faster releases of applications are in constant demand. Faster development cycles can increase the margin for errors. With machine learning, we can reduce or eliminate human errors. AI and ML are truly changing software testing as they attribute to less time consumed in manual testing- thus saving money and reducing TCO.

Are QA jobs at risk?

In a recent survey, eighty-four percent of respondents believed that existing workers would need to change their skill sets in order to adapt to AI.

While some QA engineers may say “100% automation” is in the near future. However, the majority consensus of testers believes manual testing will still have some importance. It will not be eliminated completely. The use of ML will aid as a tool to make better data-driven decisions rather than a complete replacement of testers.

QA testers will adapt to the paradigm shift in the industry. ML and AI will allow them to shift focus towards some of the more challenging issues in testing.

For example, the primary challenge testers face with automated tests is the frequency of functionality changes in their applications. Constant changes can add time and money as teams adapt their test scripts. With machine learning capabilities, we can train our AI-test engine to learn of the changes, document them, and adjust automatically.

Conclusion

Machine learning in software testing will be a vital asset in producing high-quality applications. Especially when demand is rapid and at scale. ML can consume big data, help reduce human errors and provide predictive forecasting data for more informed decision-making.

If you are seeking a testing solution with machine learning capabilities to alleviate manual testing issues, try Autify. One of the best features allows testers to record a test rather than writing test scripting language. This is extremely helpful and faster to perform for simple actions like mouse movements and keystrokes. If there are changes in the user interface or functionality, the test script will not break. It will learn of the change, adjust, and continue testing. There will be a report of UI changes in the dashboard for evaluation.

QA testers will adapt to the paradigm shift in the industry. ML and AI will allow them to shift focus towards some of the more challenging issues in testing.

For example, the primary challenge testers face with automated tests is the frequency of functionality changes in their applications. Constant changes can add time and money as teams adapt their test scripts. With machine learning capabilities, we can train our AI-test engine to learn of the changes, document them, and adjust automatically.