数值输入程序的失效区域识别
Identification of Failure Regions for Programs with Numeric Inputs
摘要：
失效域作为失效输入的所在，为提高测试方法的有效性提供了许多见解。故障区域还可以提供一些重要的信息，以支持其他过程，如软件调试。当测试方法检测到软件故障时，表明识别出了导致故障的输入，下一个重要的问题是如何基于该导致故障的输入识别故障区域，即，故障区域识别（IFR）。在本文中，我们介绍了一种新的IFR策略，即搜索边界（SB），以确定一个近似的故障区域的数值输入域。SB试图识别尽可能接近故障区域边界的其他故障引起输入。为了支持SB，我们提供了一个基本的过程，然后提出了两种方法，即固定方向搜索边界（FSB）和不同方向搜索边界（DSB）。此外，我们实现了一个自动化的实验平台，以集成这些方法。在实验中，我们使用一系列模拟研究和不同类型的失效区域的验证研究来评估所提出的SB方法。结果表明，我们的方法可以有效地确定一个故障区域，在有限的测试资源。
英文摘要：
Failure region, where failure-causing inputs reside, has provided many insights to enhance testing effectiveness of many testing methods. Failure region may also provide some important information to support other processes such as software debugging. When a testing method detects a software failure, indicating that a failure-causing input is identified, the next important question is about how to identify the failure region based on this failure-causing input, i.e., Identification of Failure Regions (IFR). In this paper, we introduce a new IFR strategy, namely Search for Boundary (SB), to identify an approximate failure region of a numeric input domain. SB attempts to identify additional failure-causing inputs that are as close to the boundary of the failure region as possible. To support SB, we provide a basic procedure, and then propose two methods, namely Fixed-orientation Search for Boundary (FSB) and Diverse-orientation Search for Boundary (DSB). In addition, we implemented an automated experimentation platform to integrate these methods. In the experiments, we evaluated the proposed SB methods using a series of simulation studies andempirical studies with different types of failure regions. The results show that our methods can effectively identify a failure region, within the limited testing resources.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
