New technologies – New opportunities to QA

What will change in 2018 in the field of QA?

In software development there are always a lots of new opportunities and challenges coming and going. Usage and adoption of existing technologies such as Blockchain, IoT, AI and Machine Learning will continue and I’m sure some new ones will step in as well. Quality Assurance is very important part of any development (Software, Embedded, Cars, Wearables, etc.) and that means that QA has to adapt with new technologies and trends.

Even simple solutions can be very complex using e.g. clouds, apps, third-party relationships, sensitive data, Blockchain, etc. Complexity is growing, everything is turning to software, regulations (e.g. GDPR) are changing and all of this has to be delivered faster and with better quality despite smaller budgets and resources.

In this time of digital transformation QA has to transform as well. One of the biggest challenges for Quality Assurance today is to adapt to agile needs. I believe that learning and intelligence test automation will be one of the most essential things in field of QA in order to meet increasing demands on testing in agile and DevOps development models.

Symbio Quality Assurance and Testing

We, here at Symbio have updated our Quality Assurance and Testing offering for all the reasons mentioned above. Do you want know more, please contact us!

We have identified intelligent, cognitive and predictive automation as the future techniques for test automation. Consequently we utilized new technologies and combined machine learning together with artificial intelligence into our smart test automation solution for localization testing.

Intelligent Localization Quality Assurance (LQA)

Normally localization testing is perceived only as testing if the translations are correct or does the translations break the layout. In reality Localization Quality Assure is much more.

The primary procedures that comprise localization QA are internationalization, localization, globalization, linguistic, functional and (normal) Quality Assurance activities. The field of localization testing includes dozens of categories, some examples: incorrect translation, inconsistency, truncation, spelling, and invalid character.

Let’s consider the scenario that we have all the possible different error categories and that we have configured a set of applicable ML and AI tools to work with all of these categories. The combination of these is the Symbio’s intelligence LQA solution. For any given category, the LQA solution will rank translations and localizations under testing in three groups:

  • For certain error-free
  • Suspicious cases – uncertain whether there’s an error or not
  • For certain error

So in a nutshell one could think of the scenario as reversed: the algorithm is not really finding errors, but it’s rather finding strings and translations that are error free. The rest are suspicious – could be ok, or it could be a bug. In some categories the algorithm can find certain errors too. The logic is that testers will only need to look at the suspicious cases.

Benefits of Symbio’s Intelligence LQA:

  • Reduce the overall testing effort through a phased testing approach and use of internationalized automation frameworks
  • Improve product quality by testing the design first before implementation testing, thus identifying critical defects earlier in the cycle
  • Reduce the cost of testing by identifying defects earlier and removing the dependencies on skilled resources
  • Improve time-to-market by enabling organizations to deploy new languages faster