When written requirements are given in text documents, the best way to generate test cases is by

using Natural Language Processing (NLP). Here's why:

Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used

to process textual requirements to extract relevant information and generate test cases. This method

is efficient in handling large volumes of textual data and identifying key elements necessary for

testing.

Why Not Other Options:

Analyzing source code for generating test cases: This is more suitable for white-box testing where the

code is available, but it doesn't apply to text-based requirements.

Machine learning on logs of execution: This approach is used for dynamic analysis based on system

behavior during execution rather than static textual requirements.

GUI analysis by computer vision: This is used for testing graphical user interfaces and is not

applicable to text-based requirements.

Reference: This aligns with the methodology discussed in the syllabus under the section on using AI

for generating test cases from textual requirements​​.