Bellwethers: A Baseline Method for Transfer Learning

doi:10.1109/TSE.2018.2821670

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Bellwethers: A Baseline Method for Transfer Learning

2019-11-01

Rahul Krishna *

Tim Menzies

DOI：10.1109/TSE.2018.2821670

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Abstract

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.

Keywords:

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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