Maintaining Competitive Edge Through Participation in Research and Development

Research is an integral part of any company’s development. A company in the technology industry needs to stay ahead of the curve. At Marviq, we believe that participating in scientific research projects is a great way to gain valuable insights into new trends and technologies that we can use to develop new products and services. By collaborating with top universities and industry leaders, we aim to gain valuable research insights that will benefit us and society.

For this reason, we are happy to participate in IVVES (Industrial-Grade Verification and Validation of Evolving Systems), an ITEA project involving 26 partners from 5 countries and will run for three years (2019-2022). This international research project focuses on developing methods for testing complex evolving systems using artificial intelligence and machine learning.

The three pillars of IVVES include:

  • Validation techniques for ML, including model quality, training data quality, and testing techniques for ML.
  • Validation techniques for complex evolving systems, including ML-driven testing, testing with uncertainties, and online testing and monitoring.
  • Data-driven engineering, including data collection techniques, instrumentation, and smart probes, pattern recognition for predictive maintenance and fault analysis, and data analytics in engineering and operation.

Since machine learning and artificial intelligence fall within our area of interest, we have dedicated our resources to developing TESTAR – a tool that enables scriptless automated system testing of desktop, web, and mobile applications at the GUI level.

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Joint Efforts For the Technology of the Future

The development of TESTAR, an open-source tool for scriptless test automation through a graphical user interface (GUI), started around 2011. After joining IVESS in 2019, we have partnered with the Open University of the Netherlands and the Polytechnic University of Valencia, as well as other companies such as Innspire and ING, to become the leading developers of TESTAR extensions and improvements.

Our contribution to the development of TESTAR focused on two main goals:

  • Code coverage footprints of GUI actions and steering TESTAR action selection to cover specific parts of the source code.
  • Improved TESTAR reporting.

TESTAR: Applying AI and Machine Learning to Testing Applications

During the three years of the project, Marviq’s developers and engineers have worked to advance the TESTAR application to be used for online testing. At its core, the tool is designed to check the source code of desktop, web, and mobile applications. In addition, it can also check whether the links contained in the application work as intended.

Our experts have programmed the tool and implemented machine learning techniques so that it can get better and more accurate over time. TESTAR simulates human behavior when checking applications very accurately, minimizing the number of random actions. By working in a way that mimics human behavior but is improved by constant self-learning, TESTAR can detect and point out most errors and mistakes that would otherwise be difficult to find.

A more advanced version of the AI would be able to check the correctness of links and other objects such as images or videos. Ultimately, it would be possible to use AI in such a way that the app deliberately tries to find the website’s weak points to break it – thus imitating the work of a professional tester.

By allowing TESTAR to learn and constantly adapt while behaving human-like, it is possible to test the source code and discover any bugs. At the current stage of development, the application cannot fully replace a human tester, but it contributes significantly to the quality and speed of online testing.

TESTAR: More Accessible Technical Reports

Another goal of Marviq’s contribution to the development of TESTAR was to improve its reporting capabilities. Once the tool has identified and pointed out errors in the application, it should provide the information to the tester via reports in an accessible form.

We have been working to develop a way for TESTAR to generate reports that non-technical people can understand. The tool looks for schemas that may cause problems with how the application works and displays various items. While the AI cannot detect all errors, it learns to become more accurate with each application, which means that each next test will be more thorough and precise. After a certain number of test cycles or a certain amount of time, TESTAR presents the test results as an easy-to-read report, significantly improving the testing process.

Leading Through Innovation

At the heart of any technology company is a belief in the future. At Marviq, we believe that artificial intelligence and machine learning are the future of IT. These technologies enable systems to learn and improve over time, making them more efficient and effective. They are changing the way we think about computing, and they will also change the way we work and live. We are participating in research projects to learn more about these technologies and improve our capabilities. We are sure this will help us become leaders in this field.

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