LongTest: Test Prioritization for Long Text Files

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Résumé

Text classification is a fundamental task in natural language processing (NLP) that involves assigning predefined labels to text files. While existing research has primarily focused on short texts (e.g., social media posts), the classification of long texts, such as legal documents and medical records, has gained increasing attention due to the rising demand for analyzing complex and information-rich documents. However, testing long text classification systems poses unique challenges due to the high manual labeling costs associated with annotating test inputs (long text files). Test input prioritization has emerged as a promising strategy to mitigate this challenge, which prioritizes potentially misclassified tests during the early stages of the testing process. By labeling these inputs earlier, it accelerates the debugging process and thus improves the efficiency of DNN testing. However, applying existing methods to prioritize long text files is constrained by several factors: 1) Confidence-based methods fail to leverage the rich semantic content of the test inputs (long text files); 2) mutation-based techniques are specifically designed for short texts, introducing small mutations that have negligible impacts on long text files; and 3) coverage-based methods are computationally expensive and less effective. To overcome these limitations, we propose LongTest, a novel test input prioritization approach specifically designed for long text files. LongTest introduces: 1) an embedding generation mechanism specifically designed to enhance the capture of information from the entire long text file, and 2) contrastive learning that enables more effective differentiation between misclassified and correctly classified samples, which optimizes the test prioritization process. The results demonstrate that LongTest outperforms all the compared test prioritization approaches. Specifically, compared with the existing test prioritization methods (i.e., DeepGini, VanillaSM, PCS, Ent...

Mots-clés

Test input prioritization , deep neural network , labelling