iSQI CT-AI Exam - Topic 10 Question 11 Discussion

Actual exam question for iSQI's CT-AI exam

Question #: 11
Topic #: 10

Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?

SELECT ONE OPTION

ANatural language processing on textual requirements

BAnalyzing source code for generating test cases

CMachine learning on logs of execution

DGUI analysis by computer vision

Show Suggested Answer

Suggested Answer: A

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.

by Claudio at Aug 28, 2024, 04:37 AM

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Toshia

3 months ago

Agreed, A is the way to go for generating test cases!

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Ilene

3 months ago

C seems interesting, but not really relevant here.

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Makeda

3 months ago

Surprised that people think A is the only option!

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Kimbery

4 months ago

I think B could work too, but not as effectively.

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Lindsey

4 months ago

A is definitely the best choice for this!

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Paul

4 months ago

GUI analysis sounds interesting, but I don't see how it relates to generating test cases from written requirements. I think I would go with option A too.

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Dorothy

4 months ago

I feel like machine learning on logs could be useful, but it’s more about execution than requirements. I might lean towards option A, but I’m not completely confident.

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Nida

4 months ago

I'm not entirely sure about this one. Analyzing source code seems more relevant for testing, but I guess it doesn't directly relate to generating test cases from text.

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Gerald

5 months ago

I think option A makes the most sense since it directly deals with the written requirements. I remember practicing something similar in our last mock exam.

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Lucina

5 months ago

GUI analysis by computer vision? That seems like an odd choice for this type of requirement-based testing. I'll have to rule that one out.

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Candida

5 months ago

I'm leaning towards option A. Parsing the written requirements with NLP seems like the most straightforward way to generate comprehensive test cases.

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Tony

5 months ago

Natural language processing on the requirements text sounds like the most logical choice to me. That should help identify the key test scenarios.

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Cory

5 months ago

This seems like a tricky one. I'll need to think carefully about the best approach here.

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Renato

5 months ago

I'm a bit confused on this one. Analyzing source code or using machine learning on logs doesn't seem directly relevant to the question.

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Merlyn

5 months ago

I think the <1mg> tag needs an 'id' attribute, but I'm not completely sure.

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Dortha

5 months ago

This is a tricky one. I'll need to carefully review the vendor recommendations to see if virtualization is supported for the specific SQL server version.

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Cathrine

1 year ago

A, definitely A. I mean, who needs fancy machine learning when you can just read the requirements and use your brain? It's the old-fashioned way, but it works!

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