Intelligent Code Completion
| dc.contributor.author | Waseem, D. | |
| dc.contributor.author | Pintu | |
| dc.contributor.author | Chandavarkar, B.R. | |
| dc.date.accessioned | 2026-02-06T06:36:10Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Auto complete suggestions for IDEs are widely used and often extremely helpful for inexperienced and expert developers alike. This paper proposes and illustrates an intelligent code completion system using an LSTM based Seq2Seq model that can be used in concert with traditional methods (Such as static analysis, prefix filtering, and tries) to increase the effectiveness of auto complete suggestions and help accelerate coding. © 2021, Springer Nature Switzerland AG. | |
| dc.identifier.citation | Communications in Computer and Information Science, 2021, Vol.1483, , p. 52-65 | |
| dc.identifier.issn | 18650929 | |
| dc.identifier.uri | https://doi.org/10.1007/978-3-030-91244-4_5 | |
| dc.identifier.uri | https://idr.nitk.ac.in/handle/123456789/30307 | |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | |
| dc.subject | Deep learning | |
| dc.subject | LSTM | |
| dc.subject | PBN | |
| dc.subject | RNN | |
| dc.subject | Seq2Seq | |
| dc.title | Intelligent Code Completion |