# End to End Software Engineering Research

arXiv: https://arxiv.org/abs/2112.11858 · HTML: https://mlsanity.com/paper/2112.11858/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 70 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 19 of 19)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021)
- [A parallel corpus of Python functions and documentation strings for automated code documentation and code generation](https://mlsanity.com/paper/1707.02275/) (2017)
- [Multilingual training for Software Engineering](https://mlsanity.com/paper/2112.02043/) (2021)
- [Retrieval-Based Neural Code Generation](https://mlsanity.com/paper/1808.10025/) (2018)
- [A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research](https://mlsanity.com/paper/2009.06520/) (2020)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [A Comparative Study of Programming Languages in Rosetta Code](https://mlsanity.com/paper/1409.0252/) (2014)
- [A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits](https://mlsanity.com/paper/2011.06244/) (2020)
- [Boosting Automatic Commit Classification Into Maintenance Activities By Utilizing Source Code Changes](https://mlsanity.com/paper/1711.05340/) (2017)
- [On the Impact of Programming Languages on Code Quality](https://mlsanity.com/paper/1901.10220/) (2019)
- [The Technical Debt Dataset](https://mlsanity.com/paper/1908.00827/) (2019)
- [MISIM: A Neural Code Semantics Similarity System Using the Context-Aware Semantics Structure](https://mlsanity.com/paper/2006.05265/) (2020)
- [Making the most of small Software Engineering datasets with modern machine learning](https://mlsanity.com/paper/2106.15209/) (2021)
- [On the abandonment and survival of open source projects: An empirical investigation](https://mlsanity.com/paper/1906.08058/) (2019)
- [Follow Your Nose -- Which Code Smells are Worth Chasing?](https://mlsanity.com/paper/2103.01861/) (2021)
- [ComSum: Commit Messages Summarization and Meaning Preservation](https://mlsanity.com/paper/2108.10763/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Large Scale Survey of Motivation in Software Development and Analysis of its Validity](https://mlsanity.com/paper/2404.08303/) (2024)
- [Which Alert Removals are Beneficial?](https://mlsanity.com/paper/2603.21322/) (2026)

## Abstract (arXiv)

End to end learning is machine learning starting in raw data and predicting a desired concept, with all steps done automatically. In software engineering context, we see it as starting from the source code and predicting process metrics. This framework can be used for predicting defects, code quality, productivity and more. End-to-end improves over features based machine learning by not requiring domain experts and being able to extract new knowledge. We describe a dataset of 5M files from 15k projects constructed for this goal. The dataset is constructed in a way that enables not only predicting concepts but also investigating their causes.