Mode 2 Technologies and Future Testing | HCLTech

Mode 2 Technologies and Future Testing

Mode 2 Technologies and Future Testing
January 15, 2019

Next Generation Technologies like Cloud computing, AI, robotics, big data, and analytics have substantially impacted the way QA and testing are performed. Digital transformation and digital disruption are forcing organisations to innovate as the delivery cycle time is decreasing and there is pressure to reduce the testing time. Most of the organizations are adopting Agile and DevOps. The software testing market is expected to grow at 14% CAGR in the next few years with the rise of agile testing and DevOps. The expectation from QA teams is to be more innovative about agile testing and have a knowledge of the high-grade analytical tools and frameworks, along with superior techniques.

The focus on test automation is significantly increasing because of digital transformation and digital disruption. Open source test automation solutions like Selenium and Appium for web and mobile have a high impact in test automation, driving digital transformation. Some of the latest testing domains and methodologies are mentioned below

The focus on test automation is significantly increasing because of digital transformation. 

Cloud computing

The worldwide public cloud services market is projected to grow by 21.4 percent in 2018 to a total of $186.4 billion, up from $153.5 billion in 2017, according to Gartner, Inc.

Enterprises are looking for robust testing frameworks and strategies to get the most of their investment in the Cloud. Cloud testing mainly involves function testing (System Verification testing, Acceptance testing, and Interoperability testing) and non-functional testing (Accessibility, Performance, Security, Recovery, and Scalability tests)

Cloud testing would require a combination of multiple skills (App testing, Network testing, Database testing, Infrastructure testing – Disaster recovery, backups, storage policies etc.), testing tools (performance tools – Jmeter, LoadStorm etc. Cloud security tools – Wireshark, Nessus etc.), and automation testing knowledge (Python, Selenium etc.)

Big Data

Big data testing is becoming a major challenge for organizations. There is a challenge related to virtualization, test automation, and handling very large dataset. Testing big data would need defining test strategies for structured and unstructured data, setting up the test environment, working with non-relational databases, and performing non-functional automation testing. The big data testing team will have to focus on data security, performance issues, the workload on the system due to very high data volumes, and the scalability of the data storage media. A tester needs to be strong on testing fundamentals and be aware of the architecture of database designs to analyse several performance bottlenecks and other issues.

The various open source Big data testing tools are

  • Pre-Hadoop processing - Apache Flume, Apache Nifi,Apache Sqoop, Apache Spark, Apache Pig etc.
  • Tools & methods for MapReduce Processes - MRUnit - Unit Testing for MR Jobs, Local Job Runner Testing, Pseudo-Distributed Testing, Full Integration Testing
  • Big data Analytics- Apache Falcon

Security Testing

The global Security Testing market is expected to reach $11.97 billion by 2023, growing at a CAGR of 19.9% during the forecast period. The rising number of connected devices owing to IoT and BYOD trends and speedy digitization are providing high growth opportunities for the market. Applications and data security is a major factor in adopting the next generation technologies.

Security testing would need a very specialized skill to test various aspects of the application. Major security testing focus involves Access to Application, Data Protection, Brute-Force Attack, SQL Injection, Session Management and Error handling etc.

AI based Testing

As software testing’s focus is shifting from manual testing to automation testing, Artificial Intelligence is playing a major role to improve the accuracy of overall testing through the implementation of digital technology. AI is helping in defect analysis, log analytics, test prioritisation, improvement in overall test coverage, and automated test case generation.

Some of the AI powered tools in the market are Applitools, SauceLabs, Testim, Sealights, Mabl, ReTest etc.

Continuous Testing

With adoption of digital technology, agile development, continuous testing and continuous integration are becoming the standard way of testing methodology. QA is no more an activity that is conducted towards the end of the development cycle. Traditional manual regression testing and test inspections have a lot of gaps and are not very reliable.

For better quality software, testing needs to involve both manual and automated testing throughout software development and release. The continuous testing provides greater agility, more visibility, and an increased ability to scale. It also synchronises testing with the rest of the delivery pipeline, which mitigates the risk of delivery delays.

Key takeaways

Due to the emergence of new technologies, the focus on test automation has increased and one of the key success factors in delivering high quality products. As technology continues to evolve, specialized testing skills are in demand. Instead of sequential testing, continuous integration and testing are needed for better prediction of work products.

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