Generating fake data is an essential dimension of modern software testing, as
demonstrated by the number and significance of data faking libraries. Yet,
developers of faking libraries cannot keep up with the wide range of data to be
generated for different natural languages and domains. In this paper, we assess
the ability of generative AI for generating test data in different domains. We
design three types of prompts for Large Language Models (LLMs), which perform
test data generation tasks at different levels of integrability: 1) raw test
data generation, 2) synthesizing programs in a specific language that generate
useful test data, and 3) producing programs that use state-of-the-art faker
libraries. We evaluate our approach by prompting LLMs to generate test data for
11 domains. The results show that LLMs can successfully generate realistic test
data generators in a wide range of domains at all three levels of
integrability.

通过生成 AI，我们评估了不同领域测试数据生成任务的三种水平上大型语言模型（LLMs）的能力，结果表明 LLMs 可以成功地在各种领域和三个集成性水平上生成逼真的测试数据生成器。

生成 AI 用于生成测试数据生成器

Generative AI to Generate Test Data Generators

In this paper, we propose ComGAN(ComparativeGAN) which allows the generator
in GANs to refer to the semantics of comparative samples(e.g. real data) by
comparison. ComGAN generalizes relativistic GANs by using arbitrary
architecture and mostly outperforms relativistic GANs in simple
input-concatenation architecture. To train the discriminator in ComGAN, we also
propose equality regularization, which fits the discriminator to a neutral
label for equally real or fake samples. Equality regularization highly boosts
the performance of ComGAN including WGAN while being exceptionally simple
compared to existing regularizations. Finally, we generalize comparative
samples fixed to real data in relativistic GANs toward fake data and show that
such objectives are sound in both theory and practice. Our experiments
demonstrate superior performances of ComGAN and equality regularization,
achieving the best FIDs in 7 out of 8 cases of different losses and data
against ordinary GANs and relativistic GANs.

本文提出了 ComGAN (ComparativeGAN), 通过比较相似的样本 (如真实数据), 让 GAN 中的生成器参考语义，并对比相似性，该方法推广了相对论 GAN，使用任意结构，大多数情况下优于相对论 GAN，在训练判别器时，我们还提出平等正则化，这将判别器拟合成公正标签，平等正则化在相比于现有正则化方法非常简单的同时还极大地提高了 ComGAN 的性能，包括 WGAN。最后，我们将相对论 GAN 中固定到真实数据的比较样本推广到假数据，并证明这种目标在理论和实践中都是可行的。实验结果显示，ComGAN 和平等正则化具有优越的性能，对于 8 种不同的损失和数据的 FID，在 7 种情况下优于普通的 GAN 和相对论 GAN。