Improving classifier-based effort-aware software defect prediction by reducing ranking errors
<|reference_start|>Improving classifier-based effort-aware software defect prediction by reducing ranking errors: Context: Software defect prediction utilizes historical data to direct software quality assurance resources to potentially problematic components. Effort-aware (EA) defect prediction prioritizes more bug-like components by taking cost-effectiveness into account. In other words, it is a ranking problem, however, existing ranking strategies based on classification, give limited consideration to ranking errors. Objective: Improve the performance of classifier-based EA ranking methods by focusing on ranking errors. Method: We propose a ranking score calculation strategy called EA-Z which sets a lower bound to avoid near-zero ranking errors. We investigate four primary EA ranking strategies with 16 classification learners, and conduct the experiments for EA-Z and the other four existing strategies. Results: Experimental results from 72 data sets show EA-Z is the best ranking score calculation strategy in terms of Recall@20% and Popt when considering all 16 learners. For particular learners, imbalanced ensemble learner UBag-svm and UBst-rf achieve top performance with EA-Z. Conclusion: Our study indicates the effectiveness of reducing ranking errors for classifier-based effort-aware defect prediction. We recommend using EA-Z with imbalanced ensemble learning.<|reference_end|>
@article{guo2024improving,
    title={Improving classifier-based effort-aware software defect prediction by
  reducing ranking errors},
    author={Yuchen Guo, Martin Shepperd, Ning Li},
    journal={arXiv preprint arXiv:2405.07604},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07604},
    primaryClass={cs.SE}
}