Search Based Software Testing (SBST) is a popular automated testing technique which uses a feedback mechanism to search for faults in software. Despite its popularity, it has fundamental challenges related to the design, construction and interpretation of the feedback. Neural Networks (NN) have been hugely popular in recent years for a wide range of tasks. We believe that they can address many of the issues inherent to common SBST approaches. Unfortunately, NNs require large and representative training datasets. In this work we present an SBST framework based on a deconvolutional generative neural network. Not only does it retain the beneficial qualities that make NNs appropriate for SBST tasks, it also produces its own training data which circumvents the problem of acquiring a training dataset that limits the use of NNs. We demonstrate through a series of experiments that this architecture is possible and practical. It generates diverse, sensible program inputs, while exploring the space of program behaviours. It also creates a meaningful ordering over program behaviours and is able to find crashing executions. This is all done without any prior knowledge of the program. We believe this proof of concept opens new directions for future work at the intersection of SBST and neural networks.

中文翻译：

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用于自动化软件测试的生成神经网络框架

基于搜索的软件测试 (SBST) 是一种流行的自动化测试技术，它使用反馈机制来搜索软件中的故障。尽管它很受欢迎，但它在反馈的设计、构建和解释方面存在基本挑战。近年来，神经网络 (NN) 在广泛的任务中非常受欢迎。我们相信它们可以解决常见 SBST 方法固有的许多问题。不幸的是，NN 需要大型且具有代表性的训练数据集。在这项工作中，我们提出了一个基于反卷积生成神经网络的 SBST 框架。它不仅保留了使 NN 适用于 SBST 任务的有益品质，而且还生成了自己的训练数据，从而避免了获取限制 NN 使用的训练数据集的问题。我们通过一系列实验证明这种架构是可行的和实用的。它生成多样化、合理的程序输入，同时探索程序行为的空间。它还创建了对程序行为的有意义的排序，并能够找到崩溃的执行。这一切都是在没有任何程序知识的情况下完成的。我们相信这一概念证明为 SBST 和神经网络的交叉领域的未来工作开辟了新的方向。