When less is more: on the value of “co-training” for semi-supervised software defect predictors

Empirical Software Engineering(2024)

引用 0|浏览2
暂无评分
摘要
Labeling a module defective or non-defective is an expensive task. Hence, there are often limits on how much-labeled data is available for training. Semi-supervised classifiers use far fewer labels for training models. However, there are numerous semi-supervised methods, including self-labeling, co-training, maximal-margin, and graph-based methods, to name a few. Only a handful of these methods have been tested in SE for (e.g.) predicting defects– and even there, those methods have been tested on just a handful of projects. This paper applies a wide range of 55 semi-supervised learners to over 714 projects. We find that semi-supervised “co-training methods” work significantly better than other approaches. Specifically, after labeling, just 2.5% of data, then make predictions that are competitive to those using 100% of the data. That said, co-training needs to be used cautiously since the specific choice of co-training methods needs to be carefully selected based on a user’s specific goals. Also, we warn that a commonly-used co-training method (“multi-view”– where different learners get different sets of columns) does not improve predictions (while adding too much to the run time costs 11 hours vs. 1.8 hours). It is an open question, worthy of future work, to test if these reductions can be seen in other areas of software analytics. To assist with exploring other areas, all the codes used are available at https://github.com/ai-se/Semi-Supervised .
更多
查看译文
关键词
Semi-supervised learning,SSL,Self-training,Co-training,Boosting methods,Semi-supervised preprocessing,Clustering-based semi-supervised preprocessing,Intrinsically semi-supervised methods,Graph-based methods,Co-forest,Effort aware tri-training
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要