Software Testing in 2020: Biggest Trends

The massive demand for high-quality products created in the shortest period possible made testing a crucial success factor of the software development procedure. Because of the continually evolving technology and aggressive market QA pros are in constant search of new applicable testing methods, so they can stay relevant and meet the rising customer requirements. Because of this, new approaches are steadily emerging.

Here are some of the most important applications testing trends to see in 2020.

Testing for Agile and DevOps

Agile and DevOps should be definitely mentioned among a number of the most well-known concepts in software creation. Since both DevOps and Agile practitioners focus on improving the quality of the merchandise, analyzing becomes a common area of interest for 2 groups. From the competitive software development world more and more companies select popular Agile methodologies that, consequently, has an effect on testing practices.

Specifically, Agile methodology helps to ensure that testing becomes an inevitable part of the development process rather than a distinct stage. At the exact same time, DevOps, which implements a continuous improvement cycle is aimed at reducing the duration of the testing procedures. In the future, an increasing number of businesses will adopt DevOps philosophy to enhance the standard of released products which will have a huge effect on how the testing is done.

Big Data Testing

Big information continues to gain momentum. According to the Mordor Intelligence report, the Big data technology and service market will grow from $23.1 billion dollars in 2018 to $79.5 billion bucks in 2024. While many companies work with big data today, handling considerable amounts of information remains a challenging task, so does the testing of data that is big. To be able to ensure the top quality of big data, it can't be tested only with the assistance of traditional methods, you need a well-thought-out approach. Specifically, this means a great emphasis on performance testing and operational testing of applications and applications.

Data quality is also a critical element when analyzing data that is big, therefore it ought to be always verified before the testing process starts. Undoubtedly, testing has an significant part in large information systems and the implementation of the right Big data testing approach can provide a lot of advantages for the business enterprise. This includes improving data accuracy, minimizing the losses, easing company decisions and strategizing. That is why it is not difficult to realize that huge data providers will only be popular in the future.

Testing of IoT Solutions

According to the Gartner prediction, there will be $20.6 billion connected devices by 2020 compared to $6.4 billion in 2016. This number illustrates the substantial growth as well as the requirement to get a thoughtful IoT testing strategy. World Quality Report 2018-2019 demonstrates that more than 50% of those surveyed IT companies don't have a specific strategy for testing the software with IoT elements at the moment. At the exact same time, more than half of these plan to come up with a similar strategy later on.

There are plenty of challenges to be faced in the context of IoT, however it's essential that businesses prioritize their IoT testing in the near future. Obviously this may necessitate the adoption of innovative techniques as well as the enhancement of ability of QA pros.

AI and Machine Learning

Artificial intelligence serves as a driver in several areas of technological innovations. The potential of utilizing AI for enhancing testing procedures can also be strong because machines are able to spot the software bugs in a similar or better way than people. For example, unlike humans, AI is capable to compare the displayed image and the reference picture to detect differences between them or decide if a texture is rendered correctly.

Machine learning may also make testing procedures a great deal more effective. In particular, it can be used for evaluation suite optimizations (to recognize specific test cases), predictive analytics (to predict the principal parameters of analyzing processes based on historical data), log analytics (to determine test cases which need to be performed mechanically ), and defect data (identify high-risk applications for prioritizing regression tests).

The Growth of Open-Source Tools

More and more businesses begin to use open source alternatives for their own workflow, and analyzing isn't an exception. Since open-source applications are free, a lot of people can gain access to them and make their own contribution to software quality guarantee. In addition, customization can be made quite quickly, so these types of tools can be readily adjusted to the company analyzing needs. Despite some security challenges, open-source applications will likely prevail in the software testing industry in the next several years.

Mobile Testing Automation

In accordance with GSMA, there are 5.15 billion people globally owning mobile devices today and this number is only expected to grow. The time people spend using mobiles increases which means that cellular app testing becomes even more important. Testing mobile programs have never been simple. Due to the various kinds of phones and operating systems, the same function needs to be analyzed several times until the program reaches the market. The devices with net connection are analyzed even more thoroughly in order to stop security breaches. You will find native, hybrid and web apps, each using its own specifications.

To keep up with their continuous updates, a platform for quick automated testing of mobile programs is necessary. Automation simplifies the testing procedure generally, helps speed up regression testing, making it feasible to use previously inaccessible kinds of tests. The requirement for cellular testing automation can be driven by the requirement of fast time-to-market at an extremely competitive software development market.

Testing for Blockchain Projects

Blockchain is a disruptive technology that gives companies with a great opportunity to collaborate, track resources, and share data. A recent Deloitte survey shows strong interests of companies in the blockchain solutions -- 53 percent of surveyed organizations stated that technology has become an essential priority for their company this year. At precisely the exact same time, most companies are aware of the dangers associated with the introduction of technology, like data security problems and integration with third party software.

That is the reason why they understand the necessity of effective blockchain testing strategies. Additional advancement and adoption of the technology will entail an increase in demand for QA specialists who are able to ensure the high quality and safety of the blockchain apps.