Please use this identifier to cite or link to this item:
Title: Improving classifier-based effort-aware software defect prediction by reducing ranking errors
Authors: Guo, Y
Shepperd, M
Li, N
Keywords: software engineering (cs.SE)
Issue Date: 2024
Publisher: [ACM]
Citation: Guo, Y., Shepperd, M. and Li, N. (2024) 'Improving classifier-based effort-aware software defect prediction by reducing ranking errors', International Conference on Evaluation and Assessment in Software Engineering (EASE) 2024, Salerno, Italy, 18-21 June, pp. 1 - 10.
Abstract: 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.
Other Identifiers: ORCiD: Martin Shepperd
Appears in Collections:Dept of Computer Science Embargoed Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdfEmbargoed until 18 June 2024731.9 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.