Multiobjective Testing Resource Allocation Under Uncertainty

被引:12
作者
Pietrantuono, Roberto [1 ]
Potena, Pasqualina [3 ]
Pecchia, Antonio [2 ]
Rodriguez, Daniel [5 ]
Russo, Stefano [4 ]
Fernandez-Sanz, Luis [5 ]
机构
[1] Natl Interuniv Consortium Informat, CINI, I-80126 Naples, Italy
[2] Natl Interuniv Consortium Informat, I-80126 Naples, Italy
[3] RISE SICS Vasteras, SE-72213 Vasteras, Sweden
[4] Federico II Univ Naples, Dept Elect Engn & Informat Technol, I-80125 Naples, Italy
[5] Univ Alcala, Dept Comp Sci, Alcala De Henares 28801, Spain
关键词
Optimization; software debugging; software reliability; software testing; software quality; resource management; SOFTWARE-RELIABILITY GROWTH; GENETIC ALGORITHM; OPTIMIZATION; SYSTEMS; MODEL; SELECTION; COST; DESIGN; TIME; BUG;
D O I
10.1109/TEVC.2017.2691060
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Testing resource allocation is the problem of planning the assignment of resources to testing activities of software components so as to achieve a target goal under given constraints. Existing methods build on software reliability growth models (SRGMs), aiming at maximizing reliability given time/cost constraints, or at minimizing cost given quality/time constraints. We formulate it as a multiobjective debug-aware and robust optimization problem under uncertainty of data, advancing the state-of-the-art in the following ways. Multiobjective optimization produces a set of solutions, allowing to evaluate alternative tradeoffs among reliability, cost, and release time. Debug awareness relaxes the traditional assumptions of SRGMs-in particular the very unrealistic immediate repair of detected faults-and incorporates the bug assignment activity. Robustness provides solutions valid in spite of a degree of uncertainty on input parameters. We show results with a real-world case study.
引用
收藏
页码:347 / 362
页数:16
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