Below is a Q& A with more details around the existing functionalities and what is in the product roadmap for the future.

What drove you to create the Validata Sense.ai Analytics?

It has been in our plans from the beginning to add analytics to our continuous intelligent testing platform. Reporting has been a pain point for almost every client as they wanted to be able to have a 360-view of their development and testing efforts and be able to take such decisions that bring value to the business.

There are several challenges identified by the clients that our solution is built to address.

Clients are currently testing a wide variety of apps – both modern digital apps and business applications on enterprise level. In most cases different testing frameworks are used to test and monitor these apps and teams are struggling as it is difficult to understand and combine reports from different sources.

Traditional reporting solutions offer little data integrity, visibility and control often leading to misunderstanding development and testing effort and bad decisions.

They lack in:

• Clear referencing of business process and quality goals to Code and Defects fixing
• Real-time reporting and alerting - a tool for managers to help them take informed business decisions.
• Data intelligence: data as information is scattered in different systems and accessible by isolated teams and users
• No release-level and Multi-project reporting: reporting still done through excel spreadsheets
• Lack of Test Analytics model

When you have testing initiatives spanning across multiple projects and tools it is essential to have standardised values and processes.

Please explain in more details what your Analytics solution is offering.

The move to a next-generation reporting tool seemed essential. That is why we built our Analytics platform with Agile, performance and user experience in mind. If you fail to deliver on the performance and the user experience, you will have to stand against your users and for sure will have challenges in getting the tool accepted by your teams.

We have applied AI and machine learning to analytics and their combination is the Triple A rated way of testing for success that is helping banks deliver amazing customer experiences and help retain their clients. When AI and Analytics become an integral part of the testing process, it helps to identify issues faster and focus your testing efforts on those parts that have the biggest impact on the user experience. This means to provide better information back to developers to reduce the time-to-fix, recommend what tests to perform and predict the business impact of a new release.

Essentially the tool is able to connect with all of the key data sources used by your teams and have a user interface that is easy to navigate and intuitive, while at the same time being able to handle large amounts of data.

On top of that is does not only provide diagnostic measurements and KPI’s, but also predictive analytics which is a powerful tool for driving decisions and process.

Release Intelligence Insights for QA and DevOps

It enables us to have a quick snapshot of how we are doing against our plan and identify potential risk areas and trends.

Critical questions for a Project can be addressed, such as:

• Are things improving in the Release?
• Identify the risky projects – areas with high level bugs that are not closing
• To see the project readiness and /or project bottlenecks in terms of Defects
• Take action to correct the upcoming sprints

Capacity Planning & Live impact Analysis

It stores and processes information around Sprints, as well as the associations between Changes and Impacted Processes, and historical data of the impact that previous changes brought on our project.

Leveraging AI and machine learning it analyses these data, and is able to self-calculate and display the changes or additions needed to cover each CR and also self-correct the existing Workflows. It also suggests to the user the optimal Regression Test Pack to ensure that what has been working is still working, and dynamically recalculates the Resource Capacity Plan ( Devs and QAs workload as well as Resources Availability and Cost) while also displaying how the current plan will be affected.

Risk Coverage Optimiser

It is able to recommend which manual test cases should be automated in order to meet better results i.e higher risk coverage in less time, lower cost and with less people involved. The most important test cases will be executed first, enabling you to detect bugs for the critical business areas much earlier. This way, you can take advantage of our shift-left, risk-based testing approach to mitigate the risks and test changes more efficiently.

Automated root cause analysis

Validata Sense.ai provides real time classification on ARCA (Automated root cause analysis) and AI-generated recommendations on Priority, Severity and ‘time to fix’. In doing so it results in faster routing of the defect to the right person or team for fixing.

This enables to identify any regressions as soon as possible, accelerate development and drive higher quality and productivity for your projects.

The self-optimising AI engine continuously learns from the defect reports including text pre-processing, features extraction and selection and classifier building, and through natural language processing (NLP) and optical character recognition (OCR) it transforms the text giving more relevance and context.

It also provides recommendations on who is the best developer to assign for an issue based on his skillset and experience.

Then it creates the task schedule and resource allocation based on the issue criticality, developer load and skillset and generates alerts on the overdue tasks!

What lies in the future?

We have managed to address our client’s biggest pain points and needs, but we still have lots of things in our roadmap to be implemented. As artificial intelligence and machine learning continuously advance, they also power advances in technology. A self-healing platform is the next step – able to identify and fix any issues automatically leaving humans to focus on higher level tasks.