Duplicate test cases consume the most time and resources during software testing and, when eliminated, will get our products out the door much ahead of the planned schedule. When multiple teams develop and test various product components, there will be very similar test cases with different touch points, while the test case will be the same.
In enterprise environments where teams work in parallel, duplication of test effort becomes prevalent. For instance, an authentication component team might test API responses for user login, while a dashboard team tests the same API endpoints from their interface perspective. Without proper coordination, these overlapping tests multiply across the system.
Organizations can leverage Natural Language Processing (NLP) and machine learning techniques to address this challenge. Modern NLP approaches, particularly transformer-based models, can analyze test cases beyond simple keyword matching. These models understand the semantic meaning behind test descriptions, steps, and expected outcomes, even when written differently by different teams.
The Semantic Test Case Clustering process uses SBERT (Sentence-BERT) to convert test cases into semantic embeddings. These embeddings are processed through hierarchical clustering algorithms to identify functionally equivalent tests across various departments. Based on semantic similarity analysis, this approach eliminates redundant test cases, speeds up test execution, and provides a clear vision of test coverage and hidden gaps.
By eliminating redundant test efforts, teams can focus on exploratory testing, performance validation, and other high-value activities that improve product quality. As software systems become more complex, utilizing the Semantic Test Case Clustering process for test optimization becomes increasingly crucial for efficient product delivery.
Lakshmi Vidya Peri
TMMi America Board of Director & Principal Systems Development Engineer, Dell Technologies