Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Beten fault detection and diagnostic test, there are many tests with varying degrees of diagnosability. There is trade-off beten diagnosability of the test and its length. High diagnosability test is longer and takes more time. In this work,  tried to balance both by generating test slightly longer than normal detection test but with high diagnosability, without affecting fault coverage. The order of test vector application to attain more fault and diagnostic coverage in fer test patterns is also mentioned. If more diagnosability is required, test vectors till the lor order should be used, whereas shorter test compromises on diagnosability. Our results show similar distinguishing capability as compared with a recent work but with 90% reduction in normalized time. The fault model used in this work is stuck-at fault model.
['Anupam Bhar', 'Santanu Chattopadhyay', 'Indranil Sengupta', 'Rohit Kapur']
I am writing a research paper on a idea Small Test Set Generation with High Diagnosability
[{"from": "human", "value": "I am writing a research paper on a idea Small Test Set Generation with High Diagnosability"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Beten fault detection and diagnostic test, there are many tests with varying degrees of diagnosability. There is trade-off beten diagnosability of the test and its length. High diagnosability test is longer and takes more time. In this work,  tried to balance both by generating test slightly longer than normal detection test but with high diagnosability, without affecting fault coverage. The order of test vector application to attain more fault and diagnostic coverage in fer test patterns is also mentioned. If more diagnosability is required, test vectors till the lor order should be used, whereas shorter test compromises on diagnosability. Our results show similar distinguishing capability as compared with a recent work but with 90% reduction in normalized time. The fault model used in this work is stuck-at fault model."}]