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Ever wanted to predict the possible problems in your project even before their occurrence? Want to know what you should do to optimize your process?
Predictive analytics is the ultimate answer!
When we implement predictive analytics in QA, it can predict possible future failures keeping in view the past data. By implementing machine learning and statistical algorithms, predictive analytics extracts critical information from vast data sets. Through the information, patterns are generated, which estimate future trends, which further help in identifying failure points.
There are multiple algorithms that are implemented by Predictive analytics; these algorithms are used to process data, namely, time series analysis, regression algorithms, and machine learning. QA and testing need to be managed efficiently to deliver expected results. Predictive analytics helps in streamlining the process and smoothly perform software testing.
Predictive analytics is not a one-time activity. The process needs to be conducted continuously to analyze the data generated in the software development process. Predictive analytics and tools add business value at the end of the development process.
Predictive analytics requires a good amount of data churned to deliver effective results. Quality assurance plays a key role in delivering strong solutions, which help in dealing with the customer base. Competing firms can’t risk making errors. Predictive analytics helps testing teams to cut down the testing efforts as well as the cost, which helps organizations to cut down the time to market.
Every task in software testing generates data. Each time a test is run, you create log files, log defects compiling reports. Examining the defects, the team comes to know how the results impact the user experience. The testing team can align test scenarios and identify critical issue patterns to ensure adequate coverage. When data is combined with predictive analytics algorithms, it allows you to find data patterns that help to make accurate predictions about future failures.
For example, a retail website can optimize the order processing workflow based on the data that tells on which step most of the customers leave the site. Root cause analysis of defect data helps in risk-based testing. On the basis of the types of defects, the testing team can prioritize and optimize testing to increase the pace of testing.
To mine the test case repository, the testing team can apply machine learning algorithms, which help in arriving at an optimized regression suite and are also helpful in figuring out any redundant cases — also, predictive analytics help in forecasting the future pass rate based on the previous test results.
It is critical for a business to pay attention to the customer’s feedback. In social media monitoring, sentimental analysis is crucial to understand the feedback of customers about certain applications or products. Sentimental analytic framework makes the whole process easier and quicker, which is beneficial for the business.
Customer sentiments are collected through proven means, and analytics techniques are used to arrive at insights. The team can then easily identify the reported issues such as performance issues, compatibility issues, functional issues, etc. When you pay attention to customer feedback and strategize your team to fix the problems, it creates a positive impression of your business in customers’ eyes.
Benefits of customer feedback analytics:
• Provides insights to increase efficiency and prioritize testing
• Identifies the key issues of the customers, which they face using the digital channels
• Social analytics should be one of the key components to formulate the testing strategy. As a business, you can identify the areas of focus based on negative sentiments and aids in decision making. You get a 360-degree view of the behavior of various applications in production. It also helps the testing team to increase agility, bring in customer centricity, and minimizes the risks.
When we compare the test efficiency based on real-time user inputs and product management inputs, the winner is the product management inputs. Through predictive analytics, the QA team assures that the customer is served what he needs. For example, analysis of build system data reveals the time and dependent variables, the size of the build. The information is helpful in reducing dependency and makes the build more stable.
The very first step to improve the quality is detecting defects. Predictive analytics can detect the defects in a better and assured way with the help of available data. With the usage of predictive techniques, the software team can reach to the root cause of the failures, and it can also predict the defect ranges as well as the risk of modules for future versions.
Predictive analysis is all about saving time and money. With quick defect detection, increased efficiency, you can take your product to market quickly. While analyzing the past production defects, one gets to know what kind of bugs get introduced, and one could know if they are because of some new functionality or new technology. Predictive analytics helps in providing an insight to release schedule, is the project lagging behind or on time. If the project is lagging, it shows the reasons for the delay. You can build the strategy to handle the factors causing the delay.
With predictive analytics, the testing team gets to know what is working and what not and what they can do to get the desired results. Using predictive analytics, teams can evaluate what is helpful in driving better application efficiencies, and the team will be better able to analyze what is not helpful.
Growing digital demands are pushing organizations to go for faster release cycles, and that consumes a lot of time of resources. About 99 percent of organizations face challenges when testing in their agile environments and the average automation level is only 16 percent. Predictive analytics is addressing these problems in many ways and providing reliable solutions.