End to End Software Engineering Research
Not on the map. Why?
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.
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
19 of 70 references.
References · 19
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2021CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generationcited by 744
- 2016Data Programming: Creating Large Training Sets, Quicklycited by 246
- 2021CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Taskscited by 200
- 2017A parallel corpus of Python functions and documentation strings for automated code documentation and code generationcited by 57
- 2021Multilingual training for Software Engineeringcited by 43
- 2018Retrieval-Based Neural Code Generationcited by 41
- 2020A Systematic Literature Review on the Use of Deep Learning in Software Engineering Researchcited by 35
- 2021A Survey on Machine Learning Techniques for Source Code Analysiscited by 24
- 2014A Comparative Study of Programming Languages in Rosetta Codecited by 19
- 2020A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commitscited by 11
- 2017Boosting Automatic Commit Classification Into Maintenance Activities By Utilizing Source Code Changescited by 8
7 more
- 2019On the Impact of Programming Languages on Code Qualitycited by 6
- 2019The Technical Debt Datasetcited by 6
- 2020MISIM: A Neural Code Semantics Similarity System Using the Context-Aware Semantics Structurecited by 6
- 2021Making the most of small Software Engineering datasets with modern machine learningcited by 6
- 2019On the abandonment and survival of open source projects: An empirical investigationcited by 5
- 2021Follow Your Nose -- Which Code Smells are Worth Chasing?cited by 4
- 2021ComSum: Commit Messages Summarization and Meaning Preservationcited by 3
Built on by
Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.