NDLI: Isolating Failure-Inducing Combinations in Combinatorial Testing Using Test Augmentation and Classification

Content Provider	IEEE Xplore Digital Library
Author	Shakya, K. Tao Xie Nuo Li Yu Lei Kacker, R. Kuhn, R.
Copyright Year	2012
Abstract	Combinatorial Testing (CT) is a systematic way of sampling input parameters of the software under test (SUT). A t-way combinatorial test set can exercise all behaviors of the SUT caused by interactions between t input parameters or less. Although combinatorial testing can provide fault detection capability, it is often desirable to isolate the input combinations that cause failures. Isolating these failure-inducing combinations aids developers in understanding the causes of failures. Previous work directly uses classification tree analysis on the results of combinatorial testing to model the failure inducing combinations. But in many scenarios, the effectiveness of classification depends upon whether the analyzed test set is sufficient for classification. In addition, generating combinatorial tests for more-than-6-way combination is generally expensive. To address these issues, we propose an approach that uses existing combinatorial testing results to generate additional tests that enhance the effectiveness of classification. In addition, our approach also includes a technique to reduce the complexity of the resulting classification tree so that developers can understand the nature of failure-inducing combinations. We present the preliminary results of our approach applied on the TCAS benchmark.
Starting Page	620
Ending Page	623
File Size	168098
Page Count	4
File Format	PDF
ISBN	9781457719066
e-ISBN	9780769546704
DOI	10.1109/ICST.2012.149
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-04-17
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Fault Localization Software Complexity theory Decision trees Arrays Data mining Combinatorial Testing Classification Tree Testing
Content Type	Text
Resource Type	Article

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