# Bellwethers: A Baseline Method For Transfer Learning

arXiv: https://arxiv.org/abs/1703.06218 · HTML: https://mlsanity.com/paper/1703.06218/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2017.

## Built on by (most cited first, 7 of 7)

- [Towards a consistent interpretation of AIOps models](https://mlsanity.com/paper/2202.02298/) (2022)
- [Detecting Privacy Requirements from User Stories with NLP Transfer Learning Models](https://mlsanity.com/paper/2202.01035/) (2022)
- [When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors](https://mlsanity.com/paper/2211.05920/) (2022)
- [Assessing the Early Bird Heuristic (for Predicting Project Quality)](https://mlsanity.com/paper/2105.11082/) (2021)
- [Detecting Concept Drift for the reliability prediction of Software Defects using Instance Interpretation](https://mlsanity.com/paper/2305.16323/) (2023)
- [SETrLUSI: Stochastic Ensemble Multi-Source Transfer Learning Using Statistical Invariant](https://mlsanity.com/paper/2509.15593/) (2025)
- [Is Model Instability just Noise to be Tolerated or a Property that can be Managed?](https://mlsanity.com/paper/2607.10420/) (2026)

## Abstract (arXiv)

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