AI-assisted Test Automation
ValidataSense.ai uses AI and advanced machine learning algorithms to automatically generate end-to-end test workflows with ‘fit for purpose’ test data, covering a wider range of end-user journeys, and provides real-time suggestions on the optimal testing paths, and the ‘next best steps’ for maximum coverage.
- Unlock your data
- Make them understandable and business- readable
- Automatically generate end-to-end user journeys
- Personalise your testing
CHALLENGES
Traditional test design challenges
Script-based tests will need to be maintained and updated. Over time, as new features and new product improvements are introduced, the test cases and regression suites will grow as well, making it difficult to ensure that these are up-to-date and reusable.
Scripting and coding have forced Business analysts and SMEs to stay away from test automation due to its complex requirement of programming skills.
Scripting itself leads to more complexity and a higher cost to maintain the scripts. The expenses incurred in maintaining these scripts erode the advantages that automation can bring.
We generate, we don’t create!
Unlike traditional automation that relies on manually written scripts, ValidataSense.ai automatically generates test cases from browser logs — eliminating the need for coding altogether. This means:
- Faster test creation without scripting complexities.
- Non-technical users can actively contribute to automation.
- Greater flexibility in adapting to frequent changes.
Self-Healing Automation
Our AI-driven platform ensures that test cases adapt dynamically to any application changes.
- Automated self-healing detects modifications, updating your test scenarios reducing test failures.
- Ensures high test stability and reduces ongoing maintenance efforts.
Let AI handle the complexity, so your team can focus on delivering quality at speed.
Machine Learning Models for Testing
Utilizes Association Rules automatically identified to determine the correlation of a Table with all its related tables.
It fully supports identification of Association against any object, field, etc.
From historical data it automatically groups/cluster a new defect. This information is used by our bug - hunting algorithm for augmentation of the test cases and expansion of the test coverage.
Model uses computational algorithm to determine the best – optimal Test case for a business topic.
The model uses the data from the Browser user logs and Sense.ai execution logs and it can automatically compute and create complete workflows – test cases.
The model is self learning, on continuous basis and it gives real time recommendations on updates or changes that need to be done on the test case and data design level.
Based on the Browser user logs data and Sense.ai execution logs the Model automatically recommends test data per step based. It can also produce synthetic data based on equivalence partitioning, Frequency and boundary analysis on the configuration data of the system.
The Defect Suggestions and recommendations are justified via explainable AI.
Uses ML Technics to categorize data coming from the Browser user logs and, Sense.ai execution logs and defects, transactions and User behavior automatically based Data and Logs.
