Abstract
We apply various seq2seq models on programming language correction tasks on Juliet Test Suite for C/C++ and Java of Software Assurance Reference Datasets and achieve 75% (for C/C++) and 56% (for Java) repair rates on these tasks. We introduce pyramid encoder in these seq2seq models, which significantly increases the computational efficiency and memory efficiency, while achieving similar repair rate to their nonpyramid counterparts. We successfully carry out error type classification task on ITC benchmark examples (with only 685 code instances) using transfer learning with models pretrained on Juliet Test Suite, pointing out a novel way of processing small programming language datasets.
Highlights
Programming language correction (PLC), which can provide suggestions for people to debug code, identify potential flaws in a program, and help programmers to improve their coding skills, has been an important topic in the Natural Language Processing (NLP) area
We did not finetune these parameters, because (1) we show that the overall performance of seq2seq model on PLC problem is satisfying and (2) we are more concerned about comparison between different attention mechanisms and between pyramid encoder and regular encoder
We show that seq2seq models, successful in natural language correction, are applicable in programming language correction
Summary
Programming language correction (PLC), which can provide suggestions for people to debug code, identify potential flaws in a program, and help programmers to improve their coding skills, has been an important topic in the Natural Language Processing (NLP) area. The syntax error problem is relatively well studied; most compilers are able to catch syntax errors, and correcting syntax errors manually is not difficult even for beginner programmers. The latter problem, is much more challenging due to several reasons. Recognizing and correcting these bugs requires a higher level of understanding of the code, including identifying the relationship between objects, making connections between blocks, and matching data types. These errors could be seen in even experienced programmers and can be time consuming to correct manually. This study will focus on automatic correction of these logic errors in code body that pass compiling stage
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
