Testing today & tomorrow
Technology surrounds us each and every day and many businesses and companies must digitally transform themselves in order to remain competitive. To do this they need to produce better-quality software in ever shorter time periods. Adopting new and improved tools and methods can help relieve the growing pressure this transformation creates on testers and QAs.
According to the World Quality Report 2017 -18, there are three major trends in testing and QA that are rapidly altering the landscape of the industry. The study shows that although increasing numbers of companies are adopting Agile as their development cycle, an overwhelming 99%(!) of respondents said that they are facing challenges moving away from centrally organized testing (for example, the more traditional TCoE test organization). Testers must increase the use of test automation in order to meet the demands put on them, especially as smart devices and IoT are increasing the complexity of testing, but on average only 16% of test activities are automated. Therefore, this area still remains largely under-exploited.
Looking back while planning ahead!
Because of the increased demands for testing within digital transformation, testing budgets are expected to rise from the current 25% to 32% of the total IT budget by 2020. The World Quality Report gives us some recommendations about how to make testing more efficient:
- Increase test automation
- Have a testing process that supports Agile and DevOps
- Invest into smart testing frameworks
- Introduce testing as early as possible into the development process
A separate article, recently published by Experimentus, specialists that help organizations build and implement software quality processes, makes these same points almost exactly, including the need to increase of testing budgets. They also expand on them by including big data as a challenge for many companies, but also as an opportunity for testers with very unique skills for testing complex data streams, as well as pointing out that artificial intelligence might become very useful for navigation in these sophisticated systems, where testing approaches are driven by data volume and variety, rather than testing scenarios and use-cases.