Enhancing FSCS-ART through Test Input Quantization and Inverted Lists

Fixed-size-candidate-set adaptive random testing (FSCS-ART) is an ART technique well-known for its best failure-detection effectiveness and usages in testing many real-life applications. However, it faces substantial computational overhead in terms of O(n2) time cost for generating n test inputs, which becomes worse for high dimensional input domains (number of inputs a software takes). As real-life programs generally have low failure-rates and have high dimensional input domains, it is vital to reduce the computational overhead while preserving the failure-detection effectiveness for efficient software testing. In this work, we adopted Quantization and InVerted File structure approach to enhance the original FSCS-ART, called QIVFSCS-ART. The proposed method preprocesses the software input domain by partitioning it into discrete cells by using K-means clustering using a uniform random dataset. After this, the quantized form of each executed test input is stored in the inverted list of its cell’s center, called centroid. Results show that the proposed method significantly relieves the computational overhead of FSCS-ART while preserving its failure-detection effectiveness, especially for the high-dimensional software input domains.