Automation in software testing has existed for some time now. With increasing expectations for faster releases and fast updates, manual software testing no longer cuts it. Therefore, organizations shift towards developing and testing automatic software.
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Seeing the process of testing the traditional automatic software in the SDLC raises the awareness that it does not produce the desired results with investment benefits that have been included in it. The main reason for this emerged as an organization still followed the waterfall software development methodology where software testing came in the end. After recognizing the need to shift the QA process at the beginning of the software development cycle, the organization began to embrace what is called quality engineering.
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Software engineering is about shifting the focus of checking quality in the end to ensure that the quality is built into the code while being developed. Having software testing is running parallel with the development process, with the help of automation, allowing organizations to get rid of obstacles posed by the methodology of development software inheritance.
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Quality engineering practices driven by automation are despite a relatively efficient way to accelerate speed to market and maintain changing customer demand, there is still a large space for further improvement. For example, to automate test cases, most of the time must be invested to identify, prioritize, and write a test case. This process sometimes takes longer than the actual development itself. Therefore, the need for more efficient and faster ways to implement automation appear, which brings artificial intelligence and engine learning into the image.
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Introducing cognitive capabilities into quality engineering
By keeping the views that look forward, we can hope to have a complete range of cognitive computing that crosses in a quality engineering life cycle, which will include technology such as deep learning, self-healing, and natural language processing, in addition to intelligence and artificial machine learning.
The introduction of AI into quality engineering allows the process of automation to carry out heavy lifts related to overall test management, while manual professionals get bandwidth to explore creative methods to improve the final quality.
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The current market dynamics have required the implementation of the Association of Agile + Devop's approach to SDLC. While agile carrying the required speed, Devop promotes a culture of collaboration and eliminating silos between departments. CI / CD pipes established with these methodologies help streamline and accelerate the development and release process. However, there are often formal metric shortcomings to measure performance and release functions.
The quality engineering driven AI and ML can produce optimization and acceleration of application quality and delivery speed, while keeping the proper KPI trail and metrics that need to be measured.
Smart ability, cognitive AI and ML algorithm allows organizations to take deformed prediction approach rather than defective recipe approach. This means, with the algorithm time can predict areas where defects can occur and allow developers to fix them proactively. Take predictions, instead of prescriptive approaches, saving considerable time in the SDLC as a whole by reducing the need for constant back and forth between Dev and QA for detection of defects and repairing defects.
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Furthermore, AI and ML algorithms can be used to automate the functional and non-functional aspects of software testing together with the test data environment and test suite optimization.
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