Abstract
Combinatorial testing (CT) technique could significantly reduce testing cost and increase software system quality. By using the test suite generated by CT as input to conduct black-box testing towards a system, we are able to detect interactions that trigger the system’s faults. Given a test case, there may be only part of all its parameters relevant to the defects in system and the interaction constructed by those partial parameters is key factor of triggering fault. If we can locate those parameters accurately, this will facilitate the software diagnosing and testing process. This paper proposes a novel algorithm named complete Fault Interaction Location (comFIL) to locate those interactions that cause system’s failures and meanwhile obtains the minimal set of target interactions in test suite produced by CT. By applying this method, testers can analyze and locate the factors relevant to defects of system more precisely, thus making the process of software testing and debugging easier and more efficient. The results of our empirical study indicate that comFIL performs better compared with known fault location techniques in combinatorial testing because of its improved effectiveness and precision.
Highlights
Combinatorial testing could significantly reduce test cost and increase quality of software system [1]
In complete Fault Interaction Location (comFIL) algorithm, we need to record the number p of each interaction that exists in passed test cases and the number f of each interaction that exists in failed test cases
With the increment of minimal fault interactions’ count, the Ratio becomes smaller; this means the higher probability of getting safe value for input parameters
Summary
Combinatorial testing could significantly reduce test cost and increase quality of software system [1]. Martınez et al [9] present a self-adaptive algorithm based on Errors Locating Arrays (ELA) and analyze the algorithm complexity This method could only be used under the condition that the value’s number of each parameter in software is not larger than 2. On the basis of LDA and ELA, Hagar et al [1] propose the method of Partial Covering Array (PCA) which could be used in the software with known safe value, and it presents a new combinatorial structure to generate ELA Another category is known as adaptive method [10], whose generation of additional test cases depends on the information given by the execution of original test cases. We generate additional test cases to select interactions in canFIS and the minimal fault interaction set is obtained
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