Akademik Veri Yönetim Sistemi
33rd ACM SIGSOFT International Conference on Software Testing and Analysis (ISSTA), Vienna, Avusturya, 16 - 20 Eylül 2024, ss.678-690, (Tam Metin Bildiri)
With the use of Deep Learning (DL) in safety-critical domains, the systematic testing of these systems has become a critical issue for human life. Due to the data-driven nature of Deep Neural Networks (DNNs), the effectiveness of tests is closely related to the adequacy of lest datasels. Test dala need lo be labeled, which requires manual human effort: and sometimes expert: knowledge. DL system testers aim to select the test data that will he most helpful in identifying the weaknesses of the DNN model by using resources efficiently. To help achieve this goal, we propose a test data prioritization approach based on using a meta-model that gels uncertainly metrics as input, which are derived from outputs of other base models. Integrating different uncertainly metrics helps overcome individual limitations of these metrics and be effective in a wider range of scenarios. We train the meta-models with the objective of predicting whether a test input will lead the tested model to make an incorrect prediction or not. We conducted an experimental evaluation with popular image classification datasets and DNN models lo evaluate the proposed approach. The results of the experiments demonstrate that our approach effectively prioritizes the test datasets and outperforms existing state-of-the-art test prioritization methods used in comparison. In the experiments, we evaluated the test prioritization approach from a distribution-aware perspective by generating test datasets with and without out-of-distribution data.