Paper Info
Title
Recommending related functions from API usage-based function clone structures.
Abstract
Developers need to be able to find reusable code for desired software features in a way that supports opportunistic programming for increased developer productivity. Our objective is to develop a recommendation system that provides a developer with function recommendations having functionality relevant to her development task. We employ a combination of information retrieval, static code analysis and data mining techniques to build the proposed recommendation system called FACER (Feature-driven API usage-based Code Examples Recommender). We performed an experimental evaluation on 122 projects from GitHub from selected categories to determine the accuracy of the retrieved code for related features. FACER recommended functions with a precision of 54% and 75% when evaluated using automated and manual methods respectively.
Year
DOI
Venue
2019
10.1145/3338906.3342486
ESEC/SIGSOFT FSE
Keywords
Field
DocType
code recommendation,software features,API usage,code clones
Recommender system,Static program analysis,Information retrieval,Computer science,Theoretical computer science,Software
Conference
ISBN
Citations 
PageRank 
978-1-4503-5572-8
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Shamsa Abid102.03