# Assessing the Early Bird Heuristic (for Predicting Project Quality)

arXiv: https://arxiv.org/abs/2105.11082 · HTML: https://mlsanity.com/paper/2105.11082/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 149 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 16 of 16)

- [The Impact of Automated Parameter Optimization on Defect Prediction Models](https://mlsanity.com/paper/1801.10270/) (2018)
- [The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models](https://mlsanity.com/paper/1801.10269/) (2018)
- [Tuning for Software Analytics: is it Really Necessary?](https://mlsanity.com/paper/1609.01759/) (2016)
- [Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)](https://mlsanity.com/paper/1705.03697/) (2017)
- [What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)](https://mlsanity.com/paper/1608.08176/) (2016)
- [How to "DODGE" Complex Software Analytics?](https://mlsanity.com/paper/1902.01838/) (2019)
- [Assessing Software Defection Prediction Performance: Why Using the Matthews Correlation Coefficient Matters](https://mlsanity.com/paper/2003.01182/) (2020)
- [A deep tree-based model for software defect prediction](https://mlsanity.com/paper/1802.00921/) (2018)
- [Bellwethers: A Baseline Method For Transfer Learning](https://mlsanity.com/paper/1703.06218/) (2017)
- [An Empirical Study on Software Defect Prediction with a Simplified Metric Set](https://mlsanity.com/paper/1402.3873/) (2014)
- [Applications of Psychological Science for Actionable Analytics](https://mlsanity.com/paper/1803.05067/) (2018)
- [Simpler Hyperparameter Optimization for Software Analytics: Why, How, When?](https://mlsanity.com/paper/2008.07334/) (2020)
- [Early Life Cycle Software Defect Prediction. Why? How?](https://mlsanity.com/paper/2011.13071/) (2020)
- [Selecting Fault Revealing Mutants](https://mlsanity.com/paper/1803.07901/) (2018)
- [Finding Trends in Software Research](https://mlsanity.com/paper/1608.08100/) (2016)
- [Assessing Practitioner Beliefs about Software Defect Prediction](https://mlsanity.com/paper/1912.10093/) (2019)

## Abstract (arXiv)

Before researchers rush to reason across all available data or try complex methods, perhaps it is prudent to first check for simpler alternatives. Specifically, if the historical data has the most information in some small region, perhaps a model learned from that region would suffice for the rest of the project. To support this claim, we offer a case study with 240 projects, where we find that the information in those projects "clump" towards the earliest parts of the project. A quality prediction model learned from just the first 150 commits works as well, or better than state-of-the-art alternatives. Using just this "early bird" data, we can build models very quickly and very early in the project life cycle. Moreover, using this early bird method, we have shown that a simple model (with just a few features) generalizes to hundreds of projects. Based on this experience, we doubt that prior work on generalizing quality models may have needlessly complicated an inherently simple process. Further, prior work that focused on later-life cycle data needs to be revisited since their conclusions were drawn from relatively uninformative regions. Replication note: all our data and scripts are available here: https://github.com/snaraya7/early-bird