NLP Test Script Selection for Network Node Specifications

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Software testing in communication systems is costly and time-consuming, particularly due to the complexity of communication devices and the need for manual generation of test cases, which consumes a significant portion of the software testing life cycle.

Innovation Solution

Utilizing natural language processing (NLP) and artificial intelligence (AI) to automatically generate test scripts from natural language test case specifications, eliminating the need for manual code writing and leveraging existing libraries of test scripts.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual generation of test cases is used, then test case accuracy and validation can be ensured, but time consumption and cost increase significantly

Engineering Contradiction:

Improvetest case accuracyVSAvoidtest case generation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical test case generation with an automated NLP-based system that converts natural language test specifications into executable test scripts, eliminating manual coding while maintaining accuracy through intelligent parsing and code generation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service test case generation where the NLP model automatically analyzes test specifications, selects appropriate test scripts from templates, and generates executable code without human intervention, allowing the system to serve itself in the test generation process

Inventive Principle:

Principle #25Self-service

2Reliability

If manual test case generation is used, then test coverage can be controlled, but productivity decreases due to high effort required

Engineering Contradiction:

Improvetest coverage controlVSAvoidtest case generation efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent substitutes manual test case creation with automated NLP-driven code generation that processes natural language specifications and directly produces executable test scripts, dramatically improving productivity while maintaining coverage control through template-based generation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs preliminary action by pre-defining test script templates and patterns that can be automatically instantiated from natural language specifications, allowing rapid generation of test cases without requiring manual creation from scratch

Inventive Principle:

Principle #10Preliminary action

3Loss of time

If automated test script generation is implemented, then time and cost are reduced, but system complexity increases due to NLP processing requirements

Engineering Contradiction:

Improvetest case generation timeVSAvoidNLP processing complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary NLP processing layer that acts as a mediator between natural language test specifications and executable test scripts, simplifying the overall system architecture by using standardized NLP libraries and pre-defined templates rather than custom complex processing

Inventive Principle:

Principle #24Intermediary (Mediator)

4Quantity of substance

If existing test script libraries are leveraged, then resource consumption is reduced, but difficulty in selecting appropriate scripts increases

Engineering Contradiction:

Improveresource consumptionVSAvoidtest script selection accuracy

Core Design Contradiction:

Quantity of substanceVSDifficulty of detecting and measuring

Solution Approach 1:

The patent replaces manual search and selection of test scripts with automated NLP-based matching that analyzes test specifications and automatically selects appropriate scripts from libraries using intelligent text processing and pattern recognition

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where the NLP model continuously refines script selection based on the analysis of test specifications, improving selection accuracy by learning from the context and characteristics of the test cases being generated

Inventive Principle:

Principle #23Feedback

Data Source

PatentEP4193261B1Test script generation from test specifications using natural language processing

Publication Date: 2026.02.18 TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)

Figure 1~2
Figure 3~4
Figure 5

AI summary

A method of testing a network node in a communication system according to some embodiments includes obtaining a test case specification that describes a test scenario associated with the network node and extracting textual features from the test case specification. A feature vector is generated that includes the extracted textual features, and the feature vector is mapped to a plurality of available test scripts. The method selects one or more of the plurality of available test scripts for execution in response to the mapping of the feature vector. For one or more of the selected test scripts the method generates a confidence score that measures a confidence of an association of the test case specification with the selected test script. Related systems are disclosed.