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Predictive Analytics is a concept which is being broadly exercised across industries and businesses to obtain the required inferences and to take business decisions. Traditional Software Quality Assurance (QA) is revising and taking on new responsibilities. Therefore, there is a growing demand for teams to take an analytics-based approach towards next generation QA. Organizations need to reach the goals of both quality and speed, which in turn, aggravates the pressure on development teams to understand the types of challenges and failures that might come up.

Predictive Analytics assists in extracting project or business-critical information from data sets by carrying out statistical algorithms and machine learning. It helps in estimating future aims that are advantageous for identifying failure points. It acts as a forecast which is important in QA for making proactive decisions. Predictive Analytics is an important aspect when it comes to Software Testing.

Major Reasons to Use Predictive Analytics for Software Testing

Two of the most important reasons for considering Predictive Analytics in QA and software testing is

1. The need to reach faster to the market and
2. To stay accurate as much as possible.

Some of the key reasons to adopt Predictive Analytics in Software Testing practices are briefed below:

1. Frames client centric quality assurance

It is necessary to understand the overall market scenario and consumer opinions to develop correct applications for consumers. Predictive Analytics applied in software testing assists in indicating the consumer opinion on product and applications, which in turn makes QA and software testing process much more consumer-centric. Practically, it assists teams in embracing customer feedback and deliver new solutions for a better experience. It ultimately helps enterprises meet their digital transformation objectives efficiently.

2. Simplifies intuitions for prioritizing software testing activities

Information gathered from software development, and testing process is huge. It has to be compressed and analyzed with appropriate tools. The predictive analytics can be applied to this data for various tasks such as examining defects in a test environment, evaluating impacts on customer experiences and much more. Root analysis of data helps in the identification of weak spots and predicts hotspots that require attention.

3. Increases testing efficiency and improve customer experience

Predictive Analytics helps in the early recognition of failures and defects. It assists the software tester to take precautionary action and decrease dissatisfaction amongst consumers. Increasing software testing efficiency is vital for delivering robust applications which are consistent and safe for the consumers. Using Predictive Analytics assists in attaining these goals on a steady basis.

Things to keep in mind for Software Testing

There are a few best practices that lead to successful Software Testing, which are listed below:

• Divide tests into small chunks- smaller tests are less time and resource consuming
• Perform regression tests- a swift and effective manual and/or automated regression testing is useful for quality assurance of the application.
• Conduct tests in every step of software development- improves quality of software, reduce labor and lessen quality maintenance cost.
• Do not conduct tests if you are a programmer- to lessen chances of prejudiced tests or shortage of creativity in the test.
• Keep the core code locked from developers- to reduce versioning conflicts.

Use these tips and predictive analytics for the best software testing process to assure user friendly quality product launch.

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