Supporting oracle construction via static analysis

In software testing, the program under test is usually executed with test inputs and checked against a test oracle, which is a mechanism to verify whether the program behaves as expected. Selecting the right oracle data to observe is crucial in test oracle construction. In the literature, researcher...

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Bibliographic Details
Published inProceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering pp. 178 - 189
Main Authors Junjie Chen, Yanwei Bai, Dan Hao, Lingming Zhang, Lu Zhang, Bing Xie, Hong Mei
Format Conference Proceeding
LanguageEnglish
Published ACM 01.09.2016
Subjects
Fault diagnosis
Java
oracle data selection
Probabilistic logic
Software
Software testing
static analysis
Test oracle
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Summary:In software testing, the program under test is usually executed with test inputs and checked against a test oracle, which is a mechanism to verify whether the program behaves as expected. Selecting the right oracle data to observe is crucial in test oracle construction. In the literature, researchers have proposed two dynamic approaches to oracle data selection by analyzing test execution information (e.g., variables' values or interaction information). However, collecting such information during program execution may incur extra cost. In this paper, we present the first static approach to oracle data selection, SODS (Static Oracle Data Selection). In particular, SODS first identifies the substitution relationships between candidate oracle data by constructing a probabilistic substitution graph based on the definition-use chains of the program under test, then estimates the fault-observing capability of each candidate oracle data, and finally selects a subset of oracle data with strong fault-observing capability. For programs with analyzable test code, we further extend SODS via pruning the probabilistic substitution graph based on 0-1-CFA call graph analysis. The experimental study on 11 subject systems written in C or Java demonstrates that our static approach is more effective and much more efficient than state-of-the-art dynamic approaches in most cases.
DOI:10.1145/2970276.2970366