# Better Data Labelling with EMBLEM (and how that Impacts Defect Prediction)

arXiv: https://arxiv.org/abs/1905.01719 · HTML: https://mlsanity.com/paper/1905.01719/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2019.

## Built on by (most cited first, 13 of 13)

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- [When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors](https://mlsanity.com/paper/2211.05920/) (2022)
- [Fairer Software Made Easier (using "Keys")](https://mlsanity.com/paper/2107.05088/) (2021)
- [A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning](https://mlsanity.com/paper/2211.10012/) (2022)
- [Less, but Stronger: On the Value of Strong Heuristics in Semi-supervised Learning for Software Analytics](https://mlsanity.com/paper/2302.01997/) (2023)
- [An Approach for Auto Generation of Labeling Functions for Software Engineering Chatbots](https://mlsanity.com/paper/2410.07094/) (2024)
- [SemiScope: Disentangling Classifier Tuning and Joint Optimization in Semi-Supervised Security Classification](https://mlsanity.com/paper/2607.00113/) (2026)

## Abstract (arXiv)

Standard automatic methods for recognizing problematic development commits can be greatly improved via the incremental application of human+artificial expertise. In this approach, called EMBLEM, an AI tool first explore the software development process to label commits that are most problematic. Humans then apply their expertise to check those labels (perhaps resulting in the AI updating the support vectors within their SVM learner). We recommend this human+AI partnership, for several reasons. When a new domain is encountered, EMBLEM can learn better ways to label which comments refer to real problems. Also, in studies with 9 open source software projects, labelling via EMBLEM's incremental application of human+AI is at least an order of magnitude cheaper than existing methods (≈ eight times). Further, EMBLEM is very effective. For the data sets explored here, EMBLEM better labelling methods significantly improved Popt20 and G-scores performance in nearly all the projects studied here.