Recommendation Systems for Software Engineering
By Martin P. Robillard, Robert J. Walker, Thomas Zimmermann. 2010
463 citations recorded by the source corpus.
Subfields: Information Systems, Software, Recommender Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Charles Sutton
- Miltiadis Allamanis
- Earl T. Barr
- Premkumar T. Devanbu
- Sebastian Proksch
- Sarah Nadi
- Michael Pradel
- Sven Amann
In Software
- Charles Sutton
- Miltiadis Allamanis
- Earl T. Barr
- Sebastian Proksch
- Mira Mezini
- Sarah Nadi
- Sven Amann
- Premkumar T. Devanbu
In Recommender Systems
Citations in the corpus
- An Empirical Evaluation of GitHub Copilot's Code Suggestions (2022, unknown): “There has been lots of research dedicated to improving developer productivity through code synthesis, code search, or other types of code recommender systems [2, 4, 20, 25].”
- Evaluating the evaluations of code recommender systems: A reality check (2016, methodology): “We consulted a RSSE survey [26], the recent RSSE book [27], and several recent publications in premier software engineering conferences to identify re-lated evaluation techniques.”
- Learning natural coding conventions (2014, background): “API recommenders, code suggestion and completion systems aim to help during editing, when a user may not know the name of an API she needs [63], the parameters she should pass to the API [75], or that the API call she is making needs to preceded by another [29, 70, 71, 76].”
- Recommendation Systems in Software Engineering (2014, background): “Palantír [60], Hipikat [17], Jazz, or Team Foundation Server).”