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Enhanced Constraint Handling for Reliability-Constrained Multiobjective Testing Resource Allocation

The multiobjective testing resource allocation problem (MOTRAP) is how to efficiently allocate the finite testing time to various modules, with the aim of optimizing system reliability, testing cost, and testing time simultaneously. To deal with this problem, a common approach is to use multiobjective evolutionary algorithms (MOEAs) to seek a set of tradeoff solutions between the three objectives. However, such a tradeoff set may contain a substantial proportion of solutions with very low reliability level, which consume lots of computational resources but may be valueless to the software project manager. In this article, a MOTRAP model with a prespecified reliability is first proposed. Then, new lower bounds on the testing time invested in different modules are theoretically deduced from the necessary condition for the achievement of the given reliability, based on which an exact algorithm for determining the new lower bounds is presented. Moreover, several enhanced constraint-handling techniques (ECHTs) derived from the new bounds are successively developed to be combined with MOEAs to correct and reduce the constraint violation. Finally, the proposed ECHTs are evaluated in comparison with various state-of-the-art constraint-solving approaches. The comparative results demonstrate that the proposed ECHTs can work well with MOEAs, make the search focus on the feasible region of the prespecified reliability, and provide the software project manager with better and more diverse, satisfactory choices in test planning.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.25 No.3 June 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 537 - 551

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

EVOLUTIONARY ALGORITHMS (EAS)
CONSTRAINT HANDLING
MULTIOBJECTIVE TESTING RESOURCE ALLOCATION
RELIABILITY CONSTRAINT

Info Detail Spesifik

DOI: 10.1109/TEVC.2021.3055538

Pernyataan Tanggungjawab

Zhaopin Su, Guofu Zhang, Feng Yue, Dezhi Zhan, Miqing Li, Bin Li, Xin Yao

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