English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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, but thereare numerous semi-supervised methods, including self-labeling, co-training,maximal-margin, and graph-based methods, to name a few. Only a handful of thesemethods have been tested in SE for (e.g.) predicting defects and even that,those tests have been on just a handful of projects. This paper takes a widerange of 55 semi-supervised learners and applies these to over 714 projects. Wefind that semi-supervised "co-training methods" work significantly better thanother approaches. However, co-training needs to be used with caution since thespecific choice of co-training methods needs to be carefully selected based ona user's specific goals. Also, we warn that a commonly-used co-training method("multi-view"-- where different learners get different sets of columns) doesnot improve predictions (while adding too much to the run time costs 11 hoursvs. 1.8 hours). Those cautions stated, we find using these "co-trainers," wecan label just 2.5% of data, then make predictions that are competitive tothose using 100% of the data. It is an open question worthy of future work totest if these reductions can be seen in other areas of software analytics.  All the codes used and datasets analyzed during the current study areavailable in the https://GitHub.com/Suvodeep90/Semi_Supervised_Methods.

When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors