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How does combinatorial testing perform in the real world: an empirical study
Empirical Software Engineering ( IF 4.1 ) Pub Date : 2020-04-20 , DOI: 10.1007/s10664-019-09799-2
Linghuan Hu , W. Eric Wong , D. Richard Kuhn , Raghu N. Kacker

Studies have shown that combinatorial testing (CT) can be effective for detecting faults in software systems. By focusing on the interactions between different factors of a system, CT shows its potential for detecting faults, especially those that can be revealed only by the specific combinations of values of multiple factors (multi-factor faults). However, is CT practical enough to be applied in the industry? Can it be more effective than other industry-favored techniques? Are there any challenges when applying CT in practice? These research questions remain in the context of industrial settings. In this paper, we present an empirical study of CT on five industrial systems with real faults. The details of the input space model (ISM) construction, such as factor identification and value assignment, are included. We compared the faults detected by CT with those detected by the in-house testing teams using other methods, and the results suggest that despite some challenges, CT is an effective technique to detect real faults, especially multi-factor faults, of software systems in industrial settings. Observations and lessons learned are provided to further improve the fault detection effectiveness and overcome various challenges.

中文翻译:

组合测试在现实世界中的表现如何:一项实证研究

研究表明,组合测试 (CT) 可以有效地检测软件系统中的故障。CT通过关注系统不同因素之间的相互作用,显示出其检测故障的潜力,尤其是那些只能通过多因素值的特定组合(多因素故障)才能揭示的故障。但是,CT是否足够实用,可以在行业中应用吗?它能否比其他行业青睐的技术更有效?在实践中应用CT有什么挑战吗?这些研究问题仍然存在于工业环境中。在本文中,我们对五个具有实际故障的工业系统的 CT 进行了实证研究。包括输入空间模型 (ISM) 构建的详细信息,例如因子识别和值分配。我们将 CT 检测到的故障与内部测试团队使用其他方法检测到的故障进行了比较,结果表明,尽管存在一些挑战,但 CT 是检测软件系统真实故障,尤其是多因素故障的有效技术。工业设置。提供了观察和经验教训,以进一步提高故障检测的有效性并克服各种挑战。
更新日期:2020-04-20
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