GIST: Generated Inputs Sets Transferability in Deep Learning
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866914801298440192 |
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| author | Tambon, Florian Khomh, Foutse Antoniol, Giuliano |
| author_facet | Tambon, Florian Khomh, Foutse Antoniol, Giuliano |
| contents | To foster the verifiability and testability of Deep Neural Networks (DNN), an increasing number of methods for test case generation techniques are being developed.
When confronted with testing DNN models, the user can apply any existing test generation technique. However, it needs to do so for each technique and each DNN model under test, which can be expensive. Therefore, a paradigm shift could benefit this testing process: rather than regenerating the test set independently for each DNN model under test, we could transfer from existing DNN models.
This paper introduces GIST (Generated Inputs Sets Transferability), a novel approach for the efficient transfer of test sets. Given a property selected by a user (e.g., neurons covered, faults), GIST enables the selection of good test sets from the point of view of this property among available test sets. This allows the user to recover similar properties on the transferred test sets as he would have obtained by generating the test set from scratch with a test cases generation technique. Experimental results show that GIST can select effective test sets for the given property to transfer. Moreover, GIST scales better than reapplying test case generation techniques from scratch on DNN models under test. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_00801 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | GIST: Generated Inputs Sets Transferability in Deep Learning Tambon, Florian Khomh, Foutse Antoniol, Giuliano Machine Learning Software Engineering To foster the verifiability and testability of Deep Neural Networks (DNN), an increasing number of methods for test case generation techniques are being developed. When confronted with testing DNN models, the user can apply any existing test generation technique. However, it needs to do so for each technique and each DNN model under test, which can be expensive. Therefore, a paradigm shift could benefit this testing process: rather than regenerating the test set independently for each DNN model under test, we could transfer from existing DNN models. This paper introduces GIST (Generated Inputs Sets Transferability), a novel approach for the efficient transfer of test sets. Given a property selected by a user (e.g., neurons covered, faults), GIST enables the selection of good test sets from the point of view of this property among available test sets. This allows the user to recover similar properties on the transferred test sets as he would have obtained by generating the test set from scratch with a test cases generation technique. Experimental results show that GIST can select effective test sets for the given property to transfer. Moreover, GIST scales better than reapplying test case generation techniques from scratch on DNN models under test. |
| title | GIST: Generated Inputs Sets Transferability in Deep Learning |
| topic | Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2311.00801 |