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http://bura.brunel.ac.uk/handle/2438/29008
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. |
URI: | https://bura.brunel.ac.uk/handle/2438/29008 |
Other Identifiers: | ORCiD: Martin Shepperd https://orcid.org/0000-0003-1874-6145 |
Appears in Collections: | Dept of Computer Science Embargoed Research Papers |
Files in This Item:
File | Description | Size | Format | |
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FullText.pdf | Embargoed until 18 June 2024 | 731.9 kB | Adobe PDF | View/Open |
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