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 / Pietrantuono, R.; Potena, P.; Pecchia, A.; Rodríguez, D.; Russo, Stefano; Fernández-Sanz, L.. - In: IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION. - ISSN 1089-778X. - 22:3(2018), pp. 347-362. [10.1109/TEVC.2017.2691060]

Multiobjective Testing Resource Allocation under Uncertainty

Pietrantuono, R.;Pecchia, A.;Russo Stefano;
2018

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
Multiobjective Testing Resource Allocation under Uncertainty / Pietrantuono, R.; Potena, P.; Pecchia, A.; Rodríguez, D.; Russo, Stefano; Fernández-Sanz, L.. - In: IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION. - ISSN 1089-778X. - 22:3(2018), pp. 347-362. [10.1109/TEVC.2017.2691060]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/667430
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