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.

翻译：摘要：为促进深度神经网络（DNN）的可验证性与可测试性，越来越多的测试用例生成技术正在被开发。当面对DNN模型测试时，用户可应用任意现有的测试生成技术，但这需要针对每种技术和每个被测DNN模型分别执行，成本高昂。因此，一种范式转换可能有益于该测试过程：与其为每个被测DNN模型独立重新生成测试集，不如从现有DNN模型迁移测试集。本文提出GIST（生成输入集可迁移性）——一种高效迁移测试集的新方法。给定用户选择的属性（如覆盖的神经元、故障），GIST能够从现有测试集中筛选出该属性视角下的优质测试集。这使得用户能在迁移后的测试集上获得与从头应用测试用例生成技术近似的属性表现。实验结果表明，GIST可为给定属性选择有效测试集进行迁移，且其可扩展性优于对被测DNN模型从头重新应用测试用例生成技术。