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Out of sight, out of mind: a distance-aware forgetting strategy for adaptive random testing

Science China(Information Sciences) 2017年第9期60卷 78-98页

作者： Chengying MAO Tsong Yueh CHEN Fei-Ching KUO School of Software and Communication Engineering Jiangxi University of Finance and Economics Department of Computer Science and Software Engineering Swinburne University of Technology

adaptive random testing(ART) achieves better failure-detection effectiveness than random testing by increasing the diversity of test cases. However, the intention of ensuring even spread of test cases inevitably causes an overhead problem. Although two basic forgetting strategies(i.e. random forgetting and consecutive retention) were proposed to reduce the computation cost of ART, they only considered the temporal distribution of test cases. In the paper, we presented a distance-aware forgetting strategy for the fixed size candidate set version of ART(DF-FSCS), in which the spatial distribution of test cases is taken into consideration. For a given candidate, the test cases out of its "sight" are ignored to reduce the distance computation cost. At the same time, the dynamic adjustment for partitioning and the second-round forgetting are adopted to ensure the linear complexity of DF-FSCS algorithm. Both simulation analysis and empirical study are employed to investigate the efficiency and effectiveness of DF-FSCS. The experimental results show that DF-FSCS significantly outperforms the classical ART algorithm FSCS-ART in efficiency, and has comparable failure-detection effectiveness. Compared with two basic forgetting methods, DF-FSCS is better in both efficiency and effectiveness. In contrast with a typical linear-time ART algorithm RBCVT-Fast, our algorithm requires less computational overhead and exhibits the similar failure-detection capability. In addition, DF-FSCS has more reliable performance than RBCVT-Fast in detecting failures for the programs with high-dimensional input domain.

关键词： adaptive random testing software testing test cases computational overhead diversity

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A revisit of three studies related to random testing

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Science China(Information Sciences) 2015年第5期58卷 49-57页

作者： CHEN Tsong Yueh KUO Fei-Ching TOWEY Dave ZHOU Zhi Quan Department of Computer Science and Software Engineering Swinburne University of Technology School of Computer Science The University of Nottingham Ningbo China School of Computer Science and Software Engineering University of Wollongong

Software testing is an approach that ensures the quality of software through execution, with a goal being to reveal failures and other problems as quickly as possible. Test case selection is a fundamental issue in software testing, and has generated a large body of research, especially with regards to the effectiveness of random testing(RT), where test cases are randomly selected from the software’s input domain. In this paper, we revisit three of our previous studies. The first study investigated a sufficient condition for partition testing(PT)to outperform RT, and was motivated by various controversial and conflicting results suggesting that sometimes PT performed better than RT, and sometimes the opposite. The second study aimed at enhancing RT itself, and was motivated by the fact that RT continues to be a fundamental and popular testing technique. This second study enhanced RT fault detection effectiveness by making use of the common observation that failure-causing inputs tend to cluster together, and resulted in a new family of RT techniques: adaptive random testing(ART),which is random testing with an even spread of test cases across the input domain. Following the successful use of failure-causing region contiguity insights to develop ART, we conducted a third study on how to make use of other characteristics of failure-causing inputs to develop more effective test case selection strategies. This third study revealed how best to approach testing strategies when certain characteristics of the failure-causing inputs are known, and produced some interesting and important results. In revisiting these three previous studies, we explore their unexpected commonalities, and identify diversity as a key concept underlying their *** observation further prompted us to examine whether or not such a concept plays a role in other areas of software testing, and our conclusion is that, yes, diversity appears to be one of the most important concepts in the fie

关键词： adaptive random testing diversity metamorphic testing proportional sampling strategy random testing software testing

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