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iSQI Exam CT-AI Topic 10 Question 11 Discussion

Actual exam question for iSQI's CT-AI exam
Question #: 11
Topic #: 10
[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:

Cathrine
1 months 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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Lorrie
1 months ago
GUI analysis by computer vision? Sounds like something straight out of a sci-fi movie! I'll pass on that one.
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Cecilia
1 days ago
C) Machine learning on logs of execution
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Sharee
4 days ago
B) Analyzing source code for generating test cases
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Dierdre
28 days ago
A) Natural language processing on textual requirements
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Deja
1 months ago
Machine learning on logs? That's a bit overkill, don't you think? I'd stick with the good old natural language processing approach.
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Linwood
2 months ago
Option B sounds interesting, but I'm not sure how that would work if we don't have the actual source code yet. Seems a bit premature.
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Alva
27 days ago
C) Machine learning on logs of execution
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Cary
1 months ago
B) Analyzing source code for generating test cases
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Valene
1 months ago
A) Natural language processing on textual requirements
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Ellsworth
2 months ago
I think option A is the way to go. Natural language processing can really help us extract meaningful test scenarios from those written requirements.
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Emelda
1 months ago
I'm not sure about option D. GUI analysis by computer vision might be too complex for generating test cases.
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Filiberto
1 months ago
I'm leaning towards option C. Machine learning on logs of execution could help us identify patterns for test cases.
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Adelina
1 months ago
I think option B could also be useful. Analyzing the source code can give us insights into potential test cases.
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Effie
2 months ago
I agree, option A seems like the best choice. Natural language processing can help us understand the requirements better.
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Alecia
2 months ago
I think B) Analyzing source code for generating test cases is the most accurate method.
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Gretchen
2 months ago
I personally prefer D) GUI analysis by computer vision for generating test cases.
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Alba
2 months ago
I disagree, I believe C) Machine learning on logs of execution is more effective.
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Candra
3 months ago
I think the best way is A) Natural language processing on textual requirements.
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