Adaptive Random Testing: The ART of test case diversity

By Tsong Yueh Chen, Fei-Ching Kuo, Robert G. Merkel, T. H. Tse. Journal of Systems and Software, 2010

374 citations recorded by the source corpus.

Random testing is not only a useful testing technique in itself, but also plays a core role in many other testing methods. Hence, any significant improvement to random testing has an impact throughout the software testing community. Recently, Adaptive Random Testing (ART) was proposed as an effective alternative to random testing. This paper presents a synthesis of the most important research results related to ART. In the course of our research and through further reflection, we have realised how the techniques and concepts of ART can be applied in a much broader context, which we present here. We believe such ideas can be applied in a variety of areas of software testing, and even beyond software testing. Amongst these ideas, we particularly note the fundamental role of diversity in test case selection strategies. We hope this paper serves to provoke further discussions and investigations of these ideas.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/e507c93e1d9d163de151ecfb60ae383396305b09

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

  1. Mark Harman
  2. Phil McMinn
  3. Myra B. Cohen
  4. Manuel Egele
  5. Tsong Yueh Chen
  6. John A. Clark
  7. Wolfgang Grieskamp
  8. Edmund Kieran Burke

In Software

  1. Mark Harman
  2. Phil McMinn
  3. Myra B. Cohen
  4. Manuel Egele
  5. Tsong Yueh Chen
  6. Mary Jean Harrold
  7. Saswat Anand
  8. John A. Clark

Citations in the corpus