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 multi-objective debug-aware and robust opti- mization problem under uncertainty of data, advancing the state-of-the-art in the following ways. Multi-objective optimization produces a set of solutions, allowing to evaluate alternative trade-offs 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.

Multiobjective Testing Resource Allocation under Uncertainty

Pecchia, A.;
2018-01-01

Abstract

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 multi-objective debug-aware and robust opti- mization problem under uncertainty of data, advancing the state-of-the-art in the following ways. Multi-objective optimization produces a set of solutions, allowing to evaluate alternative trade-offs 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.
2018
Debugging; Fault detection; Mathematical model; Optimization; Resource management; Testing; Uncertainty; Software; Theoretical Computer Science; Computational Theory and Mathematics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12070/44005
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