Generalizability of NLP-based Models for Modern Software Development Cross-Domain Environments. Krasniqi, R. & Do, H. In 2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering (NLBSE) co-located with ICSE, pages 11–13, Melbourne, Australia, May, 2023. IEEE. ![Generalizability of NLP-based Models for Modern Software Development Cross-Domain Environments [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Natural Language Processing (NLP) has shown to be effective for solving complex problems in the Software Engineering (SE) domain, such as building chatbots and its ability to translate multi-languages. Despite the advances allowed by NLP, there are technical loopholes that hinder its fullest potential within the SE domain. The open problem remains in their generalizability for modern software development tasks that typically operate in a dynamic environment, such as AWS and SaaS platforms. The problem with these setups is that they may not contain labeled data. This poses a challenge when applying most prominent data-centric NLP models such as BERT transformer models. This position paper highlights some of the most pressing challenges drawn between the intersection of NLP and SE domains. Our vision revolves around improving the NLP model generalizability for dynamic cross-domain environments that contain little or no labeled target-domain data. We discuss these challenges and propose a research roadmap to tackle this problem as a research community emanating from SE lenses.
@inproceedings{krasniqi_generalizability_2023,
address = {Melbourne, Australia},
title = {Generalizability of {NLP}-based {Models} for {Modern} {Software} {Development} {Cross}-{Domain} {Environments}},
isbn = {9798350301786},
url = {https://ieeexplore.ieee.org/document/10189135/},
doi = {10.1109/NLBSE59153.2023.00009},
abstract = {Natural Language Processing (NLP) has shown to be effective for solving complex problems in the Software Engineering (SE) domain, such as building chatbots and its ability to translate multi-languages. Despite the advances allowed by NLP, there are technical loopholes that hinder its fullest potential within the SE domain. The open problem remains in their generalizability for modern software development tasks that typically operate in a dynamic environment, such as AWS and SaaS platforms. The problem with these setups is that they may not contain labeled data. This poses a challenge when applying most prominent data-centric NLP models such as BERT transformer models. This position paper highlights some of the most pressing challenges drawn between the intersection of NLP and SE domains. Our vision revolves around improving the NLP model generalizability for dynamic cross-domain environments that contain little or no labeled target-domain data. We discuss these challenges and propose a research roadmap to tackle this problem as a research community emanating from SE lenses.},
urldate = {2023-07-30},
booktitle = {2023 {IEEE}/{ACM} 2nd {International} {Workshop} on {Natural} {Language}-{Based} {Software} {Engineering} ({NLBSE}) co-located with {ICSE}},
publisher = {IEEE},
author = {Krasniqi, Rrezarta and Do, Hyunsook},
month = may,
year = {2023},
keywords = {Conference Workshop Papers},
pages = {11--13},
}
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