Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mutation testing is a porful methodology for evaluating test suite quality. In mutation testing, a large number of mutants are generated and executed against the test suite to check the ratio of killed mutants. Therefore, mutation testing is widely believed to be a computationally expensive technique. To alleviate the efficiency concern of mutation testing, in this paper,  propose predictive mutation testing (PMT), the first approach to predicting mutation testing results without mutant execution. In particular, the proposed approach constructs a classification model based on a series of features related to mutants and tests, and uses the classification model to predict whether a mutant is killed or survived without executing it. PMT has been evaluated on 163 real-world projects under two application scenarios (i.e., cross-version and cross-project). The experimental results demonstrate that PMT improves the efficiency of mutation testing by up to 151.4X while incurring only a small accuracy loss when predicting mutant execution results, indicating a good tradeoff beten efficiency and effectiveness of mutation testing.
['Jie Zhang', 'Ziyi Wang', 'Lingming Zhang', 'Dan Hao', 'Lei Zang', 'Shiyang Cheng', 'Lu Zhang']
I am writing a research paper on a idea Predictive mutation testing
[{"from": "human", "value": "I am writing a research paper on a idea Predictive mutation testing"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mutation testing is a porful methodology for evaluating test suite quality. In mutation testing, a large number of mutants are generated and executed against the test suite to check the ratio of killed mutants. Therefore, mutation testing is widely believed to be a computationally expensive technique. To alleviate the efficiency concern of mutation testing, in this paper,  propose predictive mutation testing (PMT), the first approach to predicting mutation testing results without mutant execution. In particular, the proposed approach constructs a classification model based on a series of features related to mutants and tests, and uses the classification model to predict whether a mutant is killed or survived without executing it. PMT has been evaluated on 163 real-world projects under two application scenarios (i.e., cross-version and cross-project). The experimental results demonstrate that PMT improves the efficiency of mutation testing by up to 151.4X while incurring only a small accuracy loss when predicting mutant execution results, indicating a good tradeoff beten efficiency and effectiveness of mutation testing."}]