# A deep tree-based model for software defect prediction

arXiv: https://arxiv.org/abs/1802.00921 · HTML: https://mlsanity.com/paper/1802.00921/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2018.

## Built on by (most cited first, 8 of 8)

- [Performance Optimization using Multimodal Modeling and Heterogeneous GNN](https://mlsanity.com/paper/2304.12568/) (2023)
- [Comparative Code Structure Analysis using Deep Learning for Performance Prediction](https://mlsanity.com/paper/2102.07660/) (2021)
- [MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations](https://mlsanity.com/paper/2407.02238/) (2024)
- [DeepCodeProbe: Towards Understanding What Models Trained on Code Learn](https://mlsanity.com/paper/2407.08890/) (2024)
- [Bringing Structure to Naturalness: On the Naturalness of ASTs](https://mlsanity.com/paper/2504.08234/) (2025)
- [Machine Learning Techniques for Software Quality Assurance: A Survey](https://mlsanity.com/paper/2104.14056/) (2021)
- [Assessing the Early Bird Heuristic (for Predicting Project Quality)](https://mlsanity.com/paper/2105.11082/) (2021)
- [JupOtter: Cell-Level Bug Detection in Jupyter Notebooks](https://mlsanity.com/paper/2606.23877/) (2026)

## Abstract (arXiv)

Defects are common in software systems and can potentially cause various problems to software users. Different methods have been developed to quickly predict the most likely locations of defects in large code bases. Most of them focus on designing features (e.g. complexity metrics) that correlate with potentially defective code. Those approaches however do not sufficiently capture the syntax and different levels of semantics of source code, an important capability for building accurate prediction models. In this paper, we develop a novel prediction model which is capable of automatically learning features for representing source code and using them for defect prediction. Our prediction system is built upon the powerful deep learning, tree-structured Long Short Term Memory network which directly matches with the Abstract Syntax Tree representation of source code. An evaluation on two datasets, one from open source projects contributed by Samsung and the other from the public PROMISE repository, demonstrates the effectiveness of our approach for both within-project and cross-project predictions.