@ARTICLE{PietrantuonoPPRRF2018,
author={R. Pietrantuono and P. Potena and A. Pecchia and D. Rodriguez and S. Russo and L. Fern\'ndez-Sanz},
journal={IEEE Transactions on Evolutionary Computation},
title={Multiobjective Testing Resource Allocation Under Uncertainty},
year={2018},
volume={22},
number={3},
pages={347--362},
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 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.},
keywords={Debugging;Fault detection;Mathematical model;Optimization;Resource management;Testing;Uncertainty;Optimization;resource management;software debugging;software quality;software reliability;software testing},
doi={10.1109/TEVC.2017.2691060},
ISSN={1089-778X},
month={June},}