Sample-based software defect prediction with active and semi-supervised learning |
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Authors: | Ming Li Hongyu Zhang Rongxin Wu Zhi-Hua Zhou |
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Affiliation: | (1) Program of Bioengineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;(2) Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;(3) Department of Biochemistry, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;(4) Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong |
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Abstract: | Software defect prediction can help us better understand and control software quality. Current defect prediction techniques
are mainly based on a sufficient amount of historical project data. However, historical data is often not available for new
projects and for many organizations. In this case, effective defect prediction is difficult to achieve. To address this problem,
we propose sample-based methods for software defect prediction. For a large software system, we can select and test a small
percentage of modules, and then build a defect prediction model to predict defect-proneness of the rest of the modules. In
this paper, we describe three methods for selecting a sample: random sampling with conventional machine learners, random sampling
with a semi-supervised learner and active sampling with active semi-supervised learner. To facilitate the active sampling,
we propose a novel active semi-supervised learning method ACoForest which is able to sample the modules that are most helpful
for learning a good prediction model. Our experiments on PROMISE datasets show that the proposed methods are effective and
have potential to be applied to industrial practice. |
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