Many Natural Language Processing (NLP) tasks, such as sentiment analysis or syntactic parsing, have benefited from the development of word embedding models. In particular, regardless of the training algorithms, the learned embeddings have often been shown to be generalizable to different NLP tasks. In contrast, despite recent momentum on word embeddings for source code, the literature lacks evidence of their generalizability beyond the example task they have been trained for.
In this experience paper, we identify 3 potential downstream tasks, namely code comments generation, code authorship identification, and code clones detection, that source code token embedding models can be applied to. We empirically assess a recently proposed code token embedding model, namely code2vec’s token embeddings. Code2vec was trained on the task of predicting method names, and while there is potential for using the vectors it learns on other tasks, it has not been explored in literature. Therefore, we fill this gap by focusing on its generalizability for the tasks we have identified. Eventually, we show that source code token embeddings cannot be readily leveraged for the downstream tasks. Our experiments even show that our attempts to use them do not result in any improvements over less sophisticated methods. We call for more research into effective and general use of code embeddings.
Tue 12 Nov Times are displayed in time zone: (GMT-07:00) Tijuana, Baja California change
|10:40 - 11:00|
Kang Hong JinSchool of Information Systems, Singapore Management University, Tegawendé F. BissyandéSnT, University of Luxembourg, David LoSingapore Management UniversityPre-print
|11:00 - 11:20|
|11:20 - 11:40|
Experience Paper: Search-based Testing in Automated Driving Control ApplicationsACM SIGSOFT Distinguished Paper Award
Christoph GladischCorporate Research, Robert Bosch GmbH, Thomas HeinzCorporate Research, Robert Bosch GmbH, Christian HeinzemannCorporate Research, Robert Bosch GmbH, Jens OehlerkingCorporate Research, Robert Bosch GmbH, Anne von VietinghoffCorporate Research, Robert Bosch GmbH, Tim PfitzerRobert Bosch Automotive Steering GmbH
|11:40 - 12:00|
Yan XiaoDepartment of Computer Science, City University of Hong Kong, Jacky KeungDepartment of Computer Science, City University of Hong Kong, Kwabena E. BenninBlekinge Institute of Technology, SERL Sweden, Qing MiDepartment of Computer Science, City University of Hong KongLink to publication
|12:00 - 12:10|
Nghi Duy Quoc BuiSingapore Management University, Singapore, Yijun YuThe Open University, UK, Lingxiao JiangSingapore Management UniversityPre-print
|12:10 - 12:20|