QA Analytics

It is a discipline of Quality Assurance QA that focuses on the use of data and its analysis to evaluate the quality of software.

Why use it?

The use of advanced analytics generates a competitive advantage by allowing us to better identify where the defects are located, where the most impactful defects will occur, increase the quality of the applications and improve the user experience, reducing operating times and avoiding economic losses due to failures. relevant.

Analytical QA analysts use a variety of tools and techniques to collect and analyze data, such as automated testing, performance analysis, error analysis, among others. This in order to identify problems and opportunities for improvement in the software.

By using data analytics, companies can identify and quantify quality issues, evaluate software performance, and detect security issues.

Analytical QA Objectives

Identify and quantify software defects

Evaluate software performance

Detect security problems

Improve software quality

Our Analytical QA Analysts can identify trends and patterns that may not be visible to the naked eye.

Benefits of QA Analytics:

Improve software quality

It can help identify and prevent errors before they reach users. This can help improve software quality and customer satisfaction.

Reduce risk

It can help reduce the risk of software errors or problems. This can help protect the company’s reputation and prevent financial losses.

Optimize the development process

Analytical QA can help optimize the software development process. This can help reduce development time and cost.

Analytical QA is a powerful tool that can help companies improve the quality of their software.

Analytical Tools and Techniques

Some examples of tools and techniques that can be used in analytical QA include:

  • Analysis of data

    Analytical QA professionals use data analysis tools to collect and analyze data about software. This data may include testing, performance, and usage data.

  • Statistic analysis

    Analytical QA professionals use statistical techniques to identify patterns and trends in data.

  • Data mining

    Analytical QA professionals use data mining techniques to identify relationships between variables.

  • Automated testing

    Automated testing can help collect data about software behavior.

  • Performance analysis

    Performance analysis can help identify bottlenecks and performance issues.

  • Error analysis

    Error analysis can help identify root causes/causes of errors.

Our Analytical QA analysts usually have training in computer science, mathematics and/or statistics. They also have experience using data analysis tools and techniques.

The exponential growth of data has generated increasing roles in the use of Analytics to make decisions and this has generated:

Data Intelligence is here to stay in the business fabric of the different sectors of the economy, which, in order to move forward, have gone from making decisions based on intuition to decisions based on intelligent data analysis.

Do you want to know more about the Analytical QA service?

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