Enhanced Constraint Handling for Reliability-Constrained Multiobjective Testing Resource Allocation

Zhaopin SU, Guofu ZHANG, Feng YUE, Dezhi ZHAN, Miqing LI, Bin LI, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

25 Citations (Scopus)


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. © 1997-2012 IEEE.
Original languageEnglish
Article number9340399
Pages (from-to)537-551
Number of pages15
JournalIEEE Transactions on Evolutionary Computation
Issue number3
Early online date29 Jan 2021
Publication statusPublished - Jun 2021
Externally publishedYes

Bibliographical note

This work was supported in part by the Anhui Provincial Key Research and Development Program under Grant 202004d07020011; in part by the National Natural Science Foundation of China under Grant U19B2044; in part by the Ministry of Education in China Project of Humanities and Social Sciences under Grant 19YJC870021 and Grant 18YJC870025; and in part by the Fundamental Research Funds for the Central Universities under Grant PA2020GDKC0015, Grant PA2019GDQT0008, and Grant PA2019GDPK0072.


  • Constraint handling
  • evolutionary algorithms (EAs)
  • multiobjective testing resource allocation
  • reliability constraint


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