The Predictive analysis carries to extract valuable information from the data sets with the help of the statistical algorithms and machine learning to forecast the trends and performance patterns. When it comes to software testing, predictive analytics makes everything clear about what to test and predict the quality issues before and after the production.
It is considered beneficial in case of the short development cycles of the latest DevOps projects. The predictive analysis and never-ending feedback from end-users made everything effortless for QA teams to forecast various applications’ risk levels in their DevOps pipeline.
So, analyzing the risk level of various applications is vital on the DevOps projects for the time being that the agile software teams may decide to take so many risks on the deployed applications. This is done in order to compete in the market itself or acquire the end-user feedback to approve a hypothesis.
With the help of the predictive analytics in software testing, the priority for binding the defects is based on the hazards potentially associated with the flaws. It is generally because of the running agile projects, error fixes for the low scope bugs, which will get low priority and are further scheduled as per the time availability.
Let us consider them in detail in the risk-based software testing:
First, is the probability of the defect emerging within the software and the effect of the fault when it appears.
The potential bug fixes are scheduled first and foremost. For instance, a bug in the specific module of code for the online shopping cart algorithm that sets away business from operating the transactions must be given first priority. Other than that, a bug which depicts an ignorant rounding error within that particular transaction must have a low priority.
The shift right testing indulges a lot more than bug fixes. The predictive analytics permits the agile teams to occupy the end-users in a certain way. We can take an example like in the case of shopping cart abandonment when clientele saves the items in their shopping carts, but leave even before purchasing it. Thus, in this kind of case, the agile team can make use of the analytics and the data mining techniques to flicker information from big transaction datasets to boost conversions by conducting the checkout process or by re-aiming the shoppers with the emails just after they leave the website.
Key reasons to steer your Agile Testing Strategy with Predictive Analysis
The most critical reason to go with predictive analytics is the need to reach with the pace to the market and stay sensible as much as can. Let us have a deep evaluation over here for a few reasons:
1. Building customer-centric agile testing:
This is really vital to know that the whole scenario of the market and the sentiments used in it is mainly to build accurate applications for the consumers. So, the analytics applied in agile testing helps to assess the emotions of the customer over the product and applications. So, this turns the agile testing a lot more consumer-centric, and that aids the team in denoting the areas to be focused like compatibility issues, performance issues, functional issues, or the security issues with the application.
It helps the customers in a way to grasp the customer feedback and provide the existing solutions for a better experience. It is so as there is nothing more vital than having the customer feedback and assimilating it in the agile testing activities. What this will bring is to help the enterprises to attain the goals for the digital transformation.
2. Providing vision for setting up the testing activities:
The information attained from software development and the testing process is huge and needs to be stored effectively for the betterment in the future. So, when the entire information got assembled with the development and testing process, it needs to be stored and determined with the accurate tools. This data can include defect logs, test cases, results, production incidents, application log files, and much more, which make the agile testing concerns.
The Predictive Analysis needs to be implemented over the data for several tasks like determining the defects in the test and the production environment, discovering the effect on the customer experience, recognizing the issues patterns, aligning the test scenarios, and a lot to it. Teams are also permitted to use the data to attain higher test coverage and to enhance the testing activity. Infact, the deep analysis of the affected data can easily recognize the weak spots and forecasts the hotspots within an application. It revamps the app development process workflow and identifies the spots where the application might mishap with the known data points.
3. Upgrading the testing efficiency and reinforce the customer experience:
We have already discussed motivating and developing customer-centric agile testing with Predictive Analytics. The agile testing teams align their work with the log files, tools, and produce the test scripts to get the relevant solutions. Therefore, it is capable of recognizing potential failures and defects. The idea of the left shift approach in the testing is to detect the errors and reduce the flaws in the future. So, predictive analytics can boost the process with the QA and agile testing teams. It would also help the teams to take the necessary actions to prevent and make the threats or the disapproval down.
Therefore, it is important to build up the efficiency of the testing to produce robust applications that are adaptable and secure for the customers. This should be a continuing process in order to provide support to the transformation activities and transfer the desired experience directly.
So, navigate your agile testing strategy with the help of predictive analytics. The power of predictive analytics and its ability to take the necessary actions has enabled enterprises to embrace collective data solutions.
Technical Writer at Oxagile
Olga Ezzheva is a technical writer at Oxagile, a leading software development company. A tech enthusiast, Olga covers a host of topics – from Big Data to Machine Learning to Computer Vision – while focusing on innovative ways to leverage technology for business growth.