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iSQI Exam CT-AI Topic 2 Question 13 Discussion

Actual exam question for iSQI's CT-AI exam
Question #: 13
Topic #: 2
[All CT-AI Questions]

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

Show Suggested Answer Hide 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.


Contribute your Thoughts:

Henriette
2 months ago
Option A is the way to go, no doubt. Anything involving machine learning or computer vision just seems like overkill for this kind of task.
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Theola
2 months ago
I bet the answer is option A. That's the most straightforward and logical approach to this problem. Natural language processing FTW!
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Lang
2 months ago
Option D sounds interesting, but I'm not sure how well computer vision would work for generating test cases. Seems a bit gimmicky to me.
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Elsa
2 months ago
Haha, option C is just lazy. Let the machine do all the work? No way, that's not how real testing is done!
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Antonio
12 days ago
D) GUI analysis by computer vision
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Idella
14 days ago
B) Analyzing source code for generating test cases
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Latia
1 months ago
A) Natural language processing on textual requirements
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Gladys
2 months ago
But wouldn't analyzing the source code for generating test cases be more accurate?
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Rhea
2 months ago
I disagree, I believe C) Machine learning on logs of execution is the best option.
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Gladys
3 months ago
I think the best way is A) Natural language processing on textual requirements.
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Lilli
3 months ago
I don't know, option B sounds like it could work too. Analyzing the source code might give you some good insights into the implementation details that could inform your test cases.
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Elliot
2 months ago
D) GUI analysis by computer vision
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Orville
2 months ago
I agree, analyzing the source code could provide valuable information for creating test cases.
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Aileen
2 months ago
B) Analyzing source code for generating test cases
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Lindsey
2 months ago
A) Natural language processing on textual requirements
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Katie
3 months ago
I think option A is the best way to generate test cases from textual requirements. Natural language processing can help identify key terms, actions, and conditions that can be translated into test cases.
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Glenn
2 months ago
Machine learning on logs of execution could be useful, but I still think option A is the best choice.
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Margart
2 months ago
Analyzing source code might be too complex, I prefer option A for generating test cases.
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Delmy
2 months ago
I think using natural language processing can help streamline the process of creating test cases.
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Nichelle
2 months ago
I agree, option A seems like the most efficient way to generate test cases.
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