Paper Info
Title
Type error feedback via analytic program repair
Abstract
We introduce Analytic Program Repair, a data-driven strategy for providing feedback for type-errors via repairs for the erroneous program. Our strategy is based on insight that similar errors have similar repairs. Thus, we show how to use a training dataset of pairs of ill-typed programs and their fixed versions to: (1) learn a collection of candidate repair templates by abstracting and partitioning the edits made in the training set into a representative set of templates; (2) predict the appropriate template from a given error, by training multi-class classifiers on the repair templates used in the training set; (3) synthesize a concrete repair from the template by enumerating and ranking correct (e.g. well-typed) terms matching the predicted template. We have implemented our approach in Rite: a type error reporting tool for OCaml programs. We present an evaluation of the accuracy and efficiency of Rite on a corpus of 4,500 ill-typed Ocaml programs drawn from two instances of an introductory programming course, and a user-study of the quality of the generated error messages that shows the locations and final repair quality to be better than the state-of-the-art tool in a statistically-significant manner.
Year
DOI
Venue
2020
10.1145/3385412.3386005
PLDI '20: 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation London UK June, 2020
Keywords
DocType
ISBN
Type Error Feedback, Program Synthesis, Program, Repair, Machine Learning
Conference
978-1-4503-7613-6
Citations 
PageRank 
References 
1
0.35
0
Authors
5
Name
Order
Citations
PageRank
Georgios Sakkas130.70
Madeline Endres233.07
Benjamin Cosman3111.86
Westley Weimer43510162.27
Ranjit Jhala52183111.68