Abstract
The present disclosure relates to a system and method for predicting software change impact and generating risk-aware quality assurance (QA) test cases. The system generates a dependency-risk graph from a collection of software delivery artifacts, such as source code, Jira tickets, and architectural documents, which captures relationships between various software components along with evidence provenance. In response to a proposed change, the system evaluates dependency paths using a risk scoring module that incorporates factors including dependency distance and historical incident data. The system identifies coverage gaps in existing test cases and generates targeted test cases for those identified gaps. Each generated test case is validated against the evidence supporting the dependency-risk graph before being published to a test management system.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Chintalapudi, Sridurga; Sahai, Sandeep; and Kummari, Prasanna, "AI Impact Predictor Using Dependency Graphs and Incident-Based Regression Test Generation", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12719