1,913 research outputs found
One-domain-one-input: adaptive random testing by orthogonal recursive bisection with restriction
One goal of software testing may be the identification
or generation of a series of test cases that can detect a fault with as few test executions as possible. Motivated by insights from research into failure-causing regions of input domains, the even-spreading (even distribution) of tests across the input domain has been identified as a useful heuristic to more quickly find failures. This finding has encouraged a shift in focus from traditional random testing (RT) to its enhancement, adaptive random testing (ART), which retains the randomness of test input selection, but also attempts to maintain a more evenly distributed spread of test inputs across the input domain. Given that there are different ways to achieve the even distribution, several different ART methods and approaches have been proposed. This paper presents a new ART method, called ART-ORB, which explores the advantages of repeated geometric bisection of the input domain, combined with restriction regions, to evenly spread test inputs. Experimental results show a better performance in terms of fewer test executions than RT to find failures. Compared with other ART methods, ART-ORB has comparable performance (in terms of required test executions), but incurs lower test input selection overheads, especially in higher dimensional input space. It is recommended that ART-ORB be used in testing situations involving expensive test input execution
Near-Optimal Recovery of Linear and N-Convex Functions on Unions of Convex Sets
In this paper we build provably near-optimal, in the minimax sense, estimates
of linear forms and, more generally, "-convex functionals" (the simplest
example being the maximum of several fractional-linear functions) of unknown
"signal" known to belong to the union of finitely many convex compact sets from
indirect noisy observations of the signal. Our main assumption is that the
observation scheme in question is good in the sense of A. Goldenshluger, A.
Juditsky, A. Nemirovski, Electr. J. Stat. 9(2) (2015), arXiv:1311.6765, the
simplest example being the Gaussian scheme where the observation is the sum of
linear image of the signal and the standard Gaussian noise. The proposed
estimates, same as upper bounds on their worst-case risks, stem from solutions
to explicit convex optimization problems, making the estimates
"computation-friendly.
A survey on adaptive random testing
Random testing (RT) is a well-studied testing method that has been widely applied to the testing of many applications, including embedded software systems, SQL database systems, and Android applications. Adaptive random testing (ART) aims to enhance RT's failure-detection ability by more evenly spreading the test cases over the input domain. Since its introduction in 2001, there have been many contributions to the development of ART, including various approaches, implementations, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on ART, classifying techniques, summarizing application areas, and analyzing experimental evaluations. This paper also addresses some misconceptions about ART, and identifies open research challenges to be further investigated in the future work
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