BACKGROUND
With the wide spread of mobile applications, embedded software, data
analytics, and cloud-based applications, the total cost of testing
software applications is huge and increasing. Both the academia and
industry are finding methods to alleviate the problem. A fundamental
element in many software testing techniques is to employ the notion of
randomness in test artifact generation or to apply the concept in the
decision making process. In a standalone manner, the notion of
randomness is realized as random testing, which is regarded as the
most basic form of software testing technique. Owing to its generality
and efficiency, and despite the wide range of findings on its
effectiveness in detecting failures, random testing has found
successful industrial applications in areas like fuzzing and stress
tests to expose software vulnerability. On the other hand, the notion
of test case diversity has been found empirically to be an important
factor in exposing failures. A significant form of test case diversity
is Adaptive Random Testing (ART), which combines the notions of
randomness and test case diversity in generating test cases. It can
improve the effectiveness of the random testing technique but incurs
the time and memory costs of test case diversity techniques. Recent
advances in ART research have produced new algorithms with linear time
complexity. Further empirical studies on real-world applications have
produced new insights, confirming that some of the ART algorithms are
consistently more effective or consistently less effective than random
testing. There has also been experimentation in replacing the notion
of randomness in other testing techniques such as test case
prioritization by novel forms of ART.
Given this preamble, IEEE Transactions on Reliability will have a
special section soliciting original work in Adaptive Random Testing
that provides innovative theoretical contributions, comprehensive
empirical validation, or novel applications. Submissions will be
reviewed and selected based on originality, technical correctness,
presentation, and practical relevance.
TOPICS
The topics of interest include, but are not limited to, the following:
+ Theoretical foundations of adaptive random testing
+ Time complexity analysis of adaptive random testing algorithms
+ Empirical study on adaptive random testing
+ Novel algorithms of adaptive random testing
+ Generalization of adaptive random testing
+ Parallelization of adaptive random test case generation
+ Novel frameworks, platforms, and kernel libraries of adaptive random testing
+ Applications of adaptive random testing to software engineering
methodology and techniques
+ Adaptive random testing for and on security, deep learning, and big
data applications
+ Novel interdisciplinary applications of adaptive random testing
+ Integration of measurement and prediction for adaptive random testing
+ Large-scale case studies, benchmark suites, and industrial
applications and practices
+ Critical evaluation of adaptive random testing
+ Adaptive random testing beyond validation and verification
SUBMISSION
We welcome high quality submissions that are original work, not
published, and not currently submitted elsewhere. We also encourage
extensions to conference papers, unless prohibited by copyright, if
there is a significant difference in the technical content.
Improvements such as adding a new case study or including a
description of additional related studies do not satisfy this
requirement. The overlapping between each submission and other
published articles, including the authors’ own papers, should be less
than 30%. Each submission must conform to the two-column format of
printed articles in the IEEE Transactions on Reliability with all
figures and tables embedded in the paper, rather than listed at the
end or in the appendix. More information on how to prepare and submit
manuscripts can be found at http://rs.ieee.org/transactions-on-reliability.html.
IMPORTANT DATES
March 1, 2018 Paper submission deadline
June 15, 2018 First round notification
December 1, 2018 Final notification
EDITOR-IN-CHIEF
Professor W. Eric Wong, University of Texas at Dallas, USA
GUEST EDITOR
Professor W.K. Chan, City University of Hong Kong, Hong Kong
GENERAL INQUIRES
Send emails to wkchan@cityu.edu.hk.