4.5 Article

Identification of Failure Regions for Programs With Numeric Inputs

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TETCI.2020.3013713

Keywords

Shape; Software; Flyback transformers; Software testing; Subspace constraints; Sun; Software debugging; software testing; failure-based testing; identification of failure region (IFR)

Funding

  1. National Natural Science Foundation of China [61872167, 61502205, U1836116]
  2. China Postdoctoral Science Foundation [2019T120396]
  3. Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX19_1614]
  4. Senior Personnel Scientific Research Foundation of Jiangsu University [14JDG039]
  5. Young Backbone Teacher Cultivation Project of Jiangsu University

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The failure region, where failure-causing inputs reside, is crucial for enhancing testing effectiveness and supporting other processes. This paper introduces a new strategy, Search for Boundary (SB), to identify an approximate failure region of a numeric input domain by identifying additional failure-causing inputs close to the boundary.
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 and empirical studies with different types of failure regions. The results show that our methods can effectively identify a failure region, within the limited testing resources.

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