# Old but Gold: Reconsidering the value of feedforward learners for software analytics

arXiv: https://arxiv.org/abs/2101.06319 · HTML: https://mlsanity.com/paper/2101.06319/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 84 references that are in the map.
- First posted on arXiv 15 Jan 2021.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017)
- [On the Number of Linear Regions of Deep Neural Networks](https://mlsanity.com/paper/1402.1869/) (2014)
- [Deep Learning with Limited Numerical Precision](https://mlsanity.com/paper/1502.02551/) (2015)
- [Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](https://mlsanity.com/paper/1909.03496/) (2019)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [Training Deep Neural Networks with 8-bit Floating Point Numbers](https://mlsanity.com/paper/1812.08011/) (2018)
- [Automated Vulnerability Detection in Source Code Using Deep Representation Learning](https://mlsanity.com/paper/1807.04320/) (2018)
- [A Convolutional Attention Network for Extreme Summarization of Source Code](https://mlsanity.com/paper/1602.03001/) (2016)
- [DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging](https://mlsanity.com/paper/1801.01275/) (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)
- [500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow)](https://mlsanity.com/paper/1802.05319/) (2018)
- [How to "DODGE" Complex Software Analytics?](https://mlsanity.com/paper/1902.01838/) (2019)
- [Checking Smart Contracts with Structural Code Embedding](https://mlsanity.com/paper/2001.07125/) (2020)
- [DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning](https://mlsanity.com/paper/1704.07734/) (2017)
- [Deep Learning in Software Engineering](https://mlsanity.com/paper/1805.04825/) (2018)
- [Evaluating prediction systems in software project estimation](https://mlsanity.com/paper/2101.05426/) (2021)
- [Simpler Hyperparameter Optimization for Software Analytics: Why, How, When?](https://mlsanity.com/paper/2008.07334/) (2020)
- [Better Predictors for Issue Lifetime](https://mlsanity.com/paper/1702.07735/) (2017)

## Built on by (most cited first, 1 of 1)

- [Is Hyper-Parameter Optimization Different for Software Analytics?](https://mlsanity.com/paper/2401.09622/) (2024)

## Abstract (arXiv)

There has been an increased interest in the use of deep learning approaches for software analytics tasks. State-of-the-art techniques leverage modern deep learning techniques such as LSTMs, yielding competitive performance, albeit at the price of longer training times. Recently, Galke and Scherp [18] showed that at least for image recognition, a decades-old feedforward neural network can match the performance of modern deep learning techniques. This motivated us to try the same in the SE literature. Specifically, in this paper, we apply feedforward networks with some preprocessing to two analytics tasks: issue close time prediction, and vulnerability detection. We test the hypothesis laid by Galke and Scherp [18], that feedforward networks suffice for many analytics tasks (which we call, the "Old but Gold" hypothesis) for these two tasks. For three out of five datasets from these tasks, we achieve new high-water mark results (that out-perform the prior state-of-the-art results) and for a fourth data set, Old but Gold performed as well as the recent state of the art. Furthermore, the old but gold results were obtained orders of magnitude faster than prior work. For example, for issue close time, old but gold found good predictors in 90 seconds (as opposed to the newer methods, which took 6 hours to run). Our results supports the "Old but Gold" hypothesis and leads to the following recommendation: try simpler alternatives before more complex methods. At the very least, this will produce a baseline result against which researchers can compare some other, supposedly more sophisticated, approach. And in the best case, they will obtain useful results that are as good as anything else, in a small fraction of the effort. To support open science, all our scripts and data are available on-line at https://github.com/fastidiouschipmunk/simple.