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To ensure the quality of the software product, software quality assurance is conducted comprising activities like planning, setting up the metrics, formal technical reviews, setting up multiple testing strategies, change control, report and documentation.

To formulate a result-oriented quality engineering strategy, enterprises may require buy-in from the management and involve the integration of test automation, AI and ML, and greater collaboration among cross-functional teams.

Software quality assurance should be robust enough to identify glitches and meet the customer experience. It should be suitably redefined to incorporate the latest methodologies and technologies.

Artificial Intelligence and machine learning can help address the growing complexity associated with QA by writing and executing automated codes and predicting the users’ needs.

The complexity surrounding software quality assurance is increasing by the day. AI can help in optimizing the processes leading to better quality products and realization of ROI.

Quality engineering is an all-encompassing discipline that looks at the macro view of development, testing and deployment of software. It is aimed at ensuring a glitch free software.

In order to stay competitive in the market, QA teams must adopt the latest trends in software product testing. The top six trends lead to a better adherence to quality.

A TCoE helps to optimize the QA processes to add value to the Software Testing, Quality Assurance, and Software Development. This blog post focuses on the value a TCoE can bring for enterprises.