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Bellwethers: A Baseline Method for Transfer Learning
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AI
DOI：10.1109/TSE.2018.2821670.png)
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摘要
En 中文
Software analytics builds quality prediction models for software projects. Experience shows that (a) the more projects studied, the more varied are the conclusions; and (b) project managers lose faith in the results of software analytics if those results keep changing. To reduce this conclusion instability, we propose the use of bellwethers: given N projects from a community the bellwether is the project whose data yields the best predictions on all others. The bellwethers offer a way to mitigate conclusion instability because conclusions about a community are stable as long as this bellwether continues as the best oracle. Bellwethers are also simple to discover (just wrap a for-loop around standard data miners). When compared to other transfer learning methods (TCA+, transfer Naive Bayes, value cognitive boosting), using just the bellwether data to construct a simple transfer learner yields comparable predictions. Further, bellwethers appear in many SE tasks such as defect prediction, effort estimation, and bad smell detection. We hence recommend using bellwethers as a baseline method for transfer learning against which future work should be compared.
Keyword:
Estimation
Software
Software engineering
Task analysis
Benchmark testing
Complexity theory
Analytical models
Transfer learning
defect prediction
bad smells
issue close time
effort estimation
prediction
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被引数:
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