PATENT CLAIM ANALYSIS

Application Number: 15916660
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-07
Patent Classification: ["717", "104000"]

Abstract:
Automated requirements-based test case generation method includes constructing a software architecture model derived from software design model architectural information, allocating requirement models into blocks/operators of the software architecture model, and generating component-level requirements-based test cases from the software architecture configured to be executable at different levels in the software architecture. The component-level requirements-based test case generation method includes receiving a software architecture along with allocated requirement models represented in hierarchical data flow diagram, selecting one of the software components, building an intermediate test model based on the selected component by automatically attaching at least one of test objectives or constraints to the corresponding software architecture model blocks/operators based on the selected test strategy, and generating human and machine readable test cases with the test generator for further automatic conversion to test executable and test review artifacts. A system and a non-transitory computer-readable medium for implementing the method are also disclosed.

Claim (Index 9):
The non-transitory computer-readable medium of  claim 8 , including instructions to cause the processor to allocate the requirement models by connecting corresponding monitored or controlled variables with an input port or an output port of respective ones of the different modules.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 90.0
- Lexical Diversity: 1.83721
- Patent Class: 717.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14947633', '15013391', '13595148', '12558375', '14819167']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3966382358142792
- 35 USC 102 Novelty (BERT): 0.5432626483589106
- Combined Prediction Score: 0.4113006770687424
- Mean Citation Score: 221.223416
- Max Citation Score: 360.99756
- Similarity Product: 319.9645626934433

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test