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AI-powered Risk Coverage Optimiser
Doing more with less
Risk is the new currency and with DevOps and Continuous Delivery, releasing with both speed and confidence, requires having immediate feedback on the business risks associated with a software release candidate.
While delivery cycle times are decreasing, the technical complexity required to deliver a positive user experience and maintain a competitive edge is increasing.
To keep up with this pace, QA teams are being asked to deliver more with less time and less resources. But there is simply not enough time to test everything – every possible customer journey before each release. In most cases application readiness is based on the number of test cases. But all tests are not created equal.
How can you be sure what is the right number of test cases or how many of your business risks are covered through testing?
Which are the optimal user journeys to test when you have a limited amount of time and budget?
If we re-assess the way we do our testing, we can achieve better coverage with much less testing. This does not mean that the quality of your applications has to suffer. In fact, advances in AI and QA can help increase the level of quality when resources and time are shrinking.
Risk is the new currency and with DevOps and Continuous Delivery, releasing with both speed and confidence, requires having immediate feedback on the business risks associated with a software release candidate.
While delivery cycle times are decreasing, the technical complexity required to deliver a positive user experience and maintain a competitive edge is increasing.
To keep up with this pace, QA teams are being asked to deliver more with less time and less resources. But there is simply not enough time to test everything – every possible customer journey before each release. In most cases application readiness is based on the number of test cases. But all tests are not created equal.
How can you be sure what is the right number of test cases or how many of your business risks are covered through testing?
Which are the optimal user journeys to test when you have a limited amount of time and budget?
If we re-assess the way we do our testing, we can achieve better coverage with much less testing. This does not mean that the quality of your applications has to suffer. In fact, advances in AI and QA can help increase the level of quality when resources and time are shrinking.
How does AI for Business Risk Coverage work?
The core of Validata Sense.ai is our AI engine which is comprised of AI and advanced machine learning algorithms, as well as a learning engine that uses Particle Swarm Optimisation (PSO) and Artificial Bee Colony (ABC) algorithms which are part of our computational intelligence technology.
Leveraging AI and machine learning, Validata Sense.ai Risk Coverage Optimiser allows you to minimize the number of test cases required for optimum coverage and lower business risk. This means that the testing activities can be aligned with the risk business objectives and the organization can achieve a higher ROI, faster time to market while reducing business risk.
It makes it safer to upgrade legacy code by showing the effect of certain upgrades and modifications of existing behaviour. It also provides a level of documentation to empower testers and developers to understand the impact of changes and make more informed decisions so further legacy challenges are prevented.
It delivers precise intelligent insights to the Project or Test Manager who would want to highlight the relevant high-risk test cases, making it the best solution for an organisation that aims to create high quality test cases automatically and streamline this challenging step in the software delivery lifecycle.
It makes it safer to upgrade legacy code by showing the effect of certain upgrades and modifications of existing behaviour. It also provides a level of documentation to empower testers and developers to understand the impact of changes and make more informed decisions so further legacy challenges are prevented.
It delivers precise intelligent insights to the Project or Test Manager who would want to highlight the relevant high-risk test cases, making it the best solution for an organisation that aims to create high quality test cases automatically and streamline this challenging step in the software delivery lifecycle.
Validata model follows the 80-20 rule
Algorithms seek
By monitoring and analysing past project data and historical trends, it predicts the performance impact and recommends which test cases should be run for maximum coverage, given constraints in time, resources and defects found.
It prioritises user journeys and identifies the most important test cases to be executed first, enabling you to detect bugs for the critical business areas much earlier.
This way, you can take advantage of a shift-left, risk-based testing approach to mitigate the risks and test changes more efficiently.
20%
of the test cases that cover
80%
of the business risk
By monitoring and analysing past project data and historical trends, it predicts the performance impact and recommends which test cases should be run for maximum coverage, given constraints in time, resources and defects found.
It prioritises user journeys and identifies the most important test cases to be executed first, enabling you to detect bugs for the critical business areas much earlier.
This way, you can take advantage of a shift-left, risk-based testing approach to mitigate the risks and test changes more efficiently.
For each test case the following features are calculated:
Frequency of the End user scenario in the bank’s business
Damage that can occur if the End user scenario is error prone and has anomalies
Number of Defects found in past runs
Risk contribution on each workflow to find the optimal workflow of one business area
The time required to run the Test Case in minutes
The coverage and data coverage of the Test Case
The cost in terms of man-days / man-hours required to run the Test Case
Budget constraints
In doing so, the user will be able to provide configurable constraints on Cost, Resources and the number of defects found.
The output is a list of the recommended Test Cases to be run with an estimation of the number of defects, along with the defects of highest Severity and Priority that are expected to be found by these Test Cases, based on past runs.
The output is a list of the recommended Test Cases to be run with an estimation of the number of defects, along with the defects of highest Severity and Priority that are expected to be found by these Test Cases, based on past runs.
AI-powered Project Risk Assessment and Analysis
The system calculates the values for each individual risk and then the Global Risk Value for the project. It is also able to calculate the Value at Risk (VaR) to estimate the maximum possible losses that may occur due to a risk.
By combining the Global Risk and VaR values, the system prioritises the projects with high risk that require attention and actions, and generates AI recommendations with possible measures to lower the risk based on Time, Quality or Cost.
By quantifying the risk of not detecting bugs and the impact of new product versions on the user before release, project stakeholders can take more informed, actionable decisions.
By combining the Global Risk and VaR values, the system prioritises the projects with high risk that require attention and actions, and generates AI recommendations with possible measures to lower the risk based on Time, Quality or Cost.
By quantifying the risk of not detecting bugs and the impact of new product versions on the user before release, project stakeholders can take more informed, actionable decisions.
Benefits
Anticipate the needs with ‘next best action’ technology
Real-time insights into your current business risk coverage and release readiness.
Maximise defect detection.
Minimise execution times.
Minimise execution times.
Accelerate your testing by minimising the number of test cases required.
Have the testing activities aligned with your business risks—all the way!
Maximise your business risk and test coverage.
Achieve higher ROI.
Achieve higher ROI.
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