一句话：E-Test 借助大语言模型识别已测试、未测试与易错单元执行场景，增量扩充测试套件。
论文关注长期演化的软件系统中测试套件难以覆盖真实生产行为的问题：只有不断加入反映实际使用情况的测试用例，才能持续维持软件质量。为此作者提出 E-Test，一种增量式测试套件扩充方法。它利用大语言模型分析单元级执行场景，区分出已被测试覆盖、尚未被测试以及容易出错的场景，并据此自动生成、补充相应的测试用例，使测试套件随着生产中新出现的行为不断演进。实验评估表明，在识别测试不充分的行为以及优化测试套件这两项任务上，E-Test 均优于当前主要的最先进方法。
Augmenting test suites with test cases that reflect the actual usage of the software system is extremely important to sustain the quality of long lasting software systems. In this paper, we propose E-Test, an approach that incrementally augments a test suite with test cases that exercise behaviors that emerge in production and that are not been tested yet. E-Test leverages Large Language Models to identify already-tested, not-yet-tested, and error-prone unit execution scenarios, and augment the test suite accordingly. Our experimental evaluation shows that E-Test outperforms the main state-of-the-art approaches to identify inadequately tested behaviors and optimize test suites.