# Deep Learning Models in Software Requirements Engineering

arXiv: https://arxiv.org/abs/2105.07771 · HTML: https://mlsanity.com/paper/2105.07771/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 21 of 21)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2001.06937/) (2020)
- [GANSynth: Adversarial Neural Audio Synthesis](https://mlsanity.com/paper/1902.08710/) (2019)
- [Improved Variational Autoencoders for Text Modeling using Dilated Convolutions](https://mlsanity.com/paper/1702.08139/) (2017)
- [Variational Neural Machine Translation](https://mlsanity.com/paper/1605.07869/) (2016)
- [Variational Recurrent Neural Machine Translation](https://mlsanity.com/paper/1801.05119/) (2018)
- [Conditional Variational Autoencoder for Neural Machine Translation](https://mlsanity.com/paper/1812.04405/) (2018)
- [Variational Template Machine for Data-to-Text Generation](https://mlsanity.com/paper/2002.01127/) (2020)
- [Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation](https://mlsanity.com/paper/2010.05119/) (2020)
- [Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks](https://mlsanity.com/paper/1709.01421/) (2017)
- [Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models](https://mlsanity.com/paper/1902.00154/) (2019)
- [Application of Low-resource Machine Translation Techniques to Russian-Tatar Language Pair](https://mlsanity.com/paper/1910.00368/) (2019)
- [Segmented and Non-Segmented Stacked Denoising Autoencoder for Hyperspectral Band Reduction](https://mlsanity.com/paper/1705.06920/) (2017)
- [SART - Similarity, Analogies, and Relatedness for Tatar Language: New Benchmark Datasets for Word Embeddings Evaluation](https://mlsanity.com/paper/1904.00365/) (2019)
- [Natural Language Processing (NLP) for Requirements Engineering: A Systematic Mapping Study](https://mlsanity.com/paper/2004.01099/) (2020)
- [Hierarchical Transformer for Multilingual Machine Translation](https://mlsanity.com/paper/2103.03589/) (2021)

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

- [MARE: Multi-Agents Collaboration Framework for Requirements Engineering](https://mlsanity.com/paper/2405.03256/) (2024)

## Abstract (arXiv)

Requirements elicitation is an important phase of any software project: the errors in requirements are more expensive to fix than the errors introduced at later stages of software life cycle. Nevertheless, many projects do not devote sufficient time to requirements. Automated requirements generation can improve the quality of software projects. In this article we have accomplished the first step of the research on this topic: we have applied the vanilla sentence autoencoder to the sentence generation task and evaluated its performance. The generated sentences are not plausible English and contain only a few meaningful words. We believe that applying the model to a larger dataset may produce significantly better results. Further research is needed to improve the quality of generated data.