Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Prompt enhance API recommendation: visualize the user’s real intention behind this query

  • Published:
Automated Software Engineering Aims and scope Submit manuscript

Abstract

Developers frequently rely on APIs in their daily programming tasks, as APIs have become an indispensable tool for program development. However, with a vast number of open-source libraries available, selecting the appropriate API quickly can be a common challenge for programmers. Previous research on API recommendation primarily focused on designing better approaches to interpret user input. However, in practical applications, it is often difficult for users, especially novice programmers, to express their real intentions due to the limitations of language expression and programming capabilities. To address this issue, this paper introduces PTAPI, an approach that visualizes the user’s real intentions based on their query to enhance recommendation performance. Firstly, PTAPI identifies the prompt template from Stack Overflow (SO) posts based on the user’s input. Secondly, the obtained prompt template is combined with the user’s input to generate a new question. Finally, the newly generated question leverages dual information sources from SO posts and API official documentation to provide recommendations. To evaluate the effectiveness of PTAPI, we conducted experiments at both the class-level and method-level. The experimental results demonstrate the effectiveness of the proposed approach, with a significant improvement in the success rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33, 1877–1901 (2020)

    Google Scholar 

  • Chen, Y., Gao, C., Ren, X., Peng, Y., Xia, X., Lyu, M.R.: API usage recommendation via multi-view heterogeneous graph representation learning. IEEE Trans. Softw. Eng. 49(5), 3289–3304 (2023)

    Article  Google Scholar 

  • Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  • Fowkes, J., Sutton, C.: Parameter-free probabilistic api mining across github. In: Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 254–265 (2016)

  • Gao, T., Fisch, A., Chen, D.: Making pre-trained language models better few-shot learners. arXiv preprint arXiv:2012.15723 (2020)

  • Gu, X., Zhang, H., Zhang, D., Kim, S.: Deep API learning. In: Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 631–642 (2016)

  • Gvero, I.: Core java volume I: fundamentals, 9th edition by cay s. horstmann and gary cornell. ACM SIGSOFT Softw. Eng. Notes 38(3), 33 (2013)

    Article  Google Scholar 

  • Hou, D., Yao, X.: Exploring the intent behind api evolution: a case study. In: 2011 18th Working Conference on Reverse Engineering, pp. 131–140. IEEE (2011)

  • Huang, Q., Xia, X., Xing, Z., Lo, D., Wang, X.: Api method recommendation without worrying about the task-API knowledge gap. In: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, pp. 293–304 (2018)

  • Irsan, I.C., Zhang, T., Thung, F., Kim, K., Lo, D.: Picaso: Enhancing API recommendations with relevant stack overflow posts. In: 2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR), 92–37 (2023)

  • Java se 8 api documentation downloading site. (2017). http://www.oracle.com/t echnetwork/java/javase/documentation/jdk8-doc-downloads-2133158.html

  • Lam, A.N., Nguyen, A.T., Nguyen, H.A., Nguyen, T.N.: Combining deep learning with information retrieval to localize buggy files for bug reports (n). In: 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 476–481 (2015)

  • Li, X.L., Liang, P.: Prefix-tuning: optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190 (2021)

  • Li, Z., Wu, J., Li, M.: Study on key issues in API usage. J. Softw. 29(6), 1716–1738 (2018)

    MathSciNet  Google Scholar 

  • Ling, C., Zou, Y., Xie, B.: Graph neural network based collaborative filtering for api usage recommendation. In: 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 36–47. IEEE (2021)

  • Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., Tang, J.: Self-supervised learning: generative or contrastive. IEEE Trans. Knowl. Data Eng. 35(1), 857–876 (2021)

    Google Scholar 

  • Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55(9), 195–119535 (2023)

    Article  Google Scholar 

  • Liu, K., Chen, X., Chen, C., Xie, X., Cui, Z.: Automated question title reformulation by mining modification logs from stack overflow. IEEE Trans. Softw. Eng. 49(9), 4390–4410 (2023)

    Article  Google Scholar 

  • Loper, E., Bird, S.: Nltk: The natural language toolkit. arXiv preprint cs/0205028 (2002)

  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26 (2013)

  • Nguyen, P.T., Di Rocco, J., Di Ruscio, D., Ochoa, L., Degueule, T., Di Penta, M.: Focus: A recommender system for mining api function calls and usage patterns. In: 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), pp. 1050–1060 (2019)

  • Nguyen, A.T., Hilton, M., Codoban, M., Nguyen, H.A., Mast, L., Rademacher, E., Nguyen, T.N., Dig, D.: API code recommendation using statistical learning from fine-grained changes. In: Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 511–522 (2016)

  • Oracle: Jdk 18 documentation, (2022). https://docs.oracle.com/en/java/javase/18/books.html

  • Ponzanelli, L., Mocci, A., Bacchelli, A., Lanza, M., Fullerton, D.: Improving low quality stack overflow post detection. In: 2014 IEEE International Conference on Software Maintenance and Evolution, pp. 541–544 (2014)

  • Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)

  • Rahman, M.M., Roy, C.K., Lo, D.: RACK: automatic API recommendation using crowdsourced knowledge. CoRR abs/1807.02953 (2018)

  • Sacramento, P., Cabral, B., Marques, P.: Unchecked exceptions: Can the programmer be trusted to document exceptions. IVNET’06 (2006)

  • Saha, R.K., Lease, M., Khurshid, S., Perry, D.E.: Improving bug localization using structured information retrieval. In: 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 345–355 (2013)

  • Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)

  • Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2008)

    Article  Google Scholar 

  • Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020)

  • Stack overflow data dump. (2017). https://archive.org/download/stackexchange

  • Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009 (2009)

  • Thung, F.: Api recommendation system for software development. In: Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, pp. 896–899 (2016)

  • Wang, J., Dang, Y., Zhang, H., Chen, K., Xie, T., Zhang, D.: Mining succinct and high-coverage api usage patterns from source code. In: 2013 10th Working Conference on Mining Software Repositories (MSR), pp. 319–328. IEEE (2013)

  • Wang, X., Zhou, K., Wen, J.-R., Zhao, W.X.: Towards unified conversational recommender systems via knowledge-enhanced prompt learning. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1929–1937 (2022)

  • Wei, M., Harzevili, N.S., Huang, Y., Wang, J., Wang, S.: Clear: contrastive learning for API recommendation. In: Proceedings of the 44th International Conference on Software Engineering, pp. 376–387 (2022)

  • Wen, M., Wu, R., Cheung, S.-C.: Locus: Locating bugs from software changes. In: Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, pp. 262–273 (2016)

  • Xie, T., Pei, J.: Mapo: mining API usages from open source repositories. In: Proceedings of the 2006 International Workshop on Mining Software Repositories, pp. 54–57 (2006)

  • Yu, Z., Bai, C., Seinturier, L., Monperrus, M.: Characterizing the usage, evolution and impact of java annotations in practice. IEEE Trans. Softw. Eng. 47(5), 969–986 (2021)

    Article  Google Scholar 

  • Zanjani, M.B., Kagdi, H., Bird, C.: Automatically recommending peer reviewers in modern code review. IEEE Trans. Softw. Eng. 42(6), 530–543 (2015)

    Article  Google Scholar 

  • Zhang, J., Jiang, H., Ren, Z., Chen, X.: Recommending APIs for API related questions in stack overflow. IEEE Access 6, 6205–6219 (2017)

    Article  Google Scholar 

  • Zhou, Y., Yang, X., Chen, T., Huang, Z., Ma, X., Gall, H.: Boosting API recommendation with implicit feedback. IEEE Trans. Softw. Eng. 48(6), 2157–2172 (2022)

    Article  Google Scholar 

Download references

Author information

Author notes

  1. Yong Wang, Cuiyun Gao, Yingtao Fang and Yong Li have contributed equally to this work.

Authors and Affiliations

  1. School of Computer and Information, Anhui Polytechnic University, Jiujiang District, Wuhu, 241000, Anhui, China

    Yong Wang, Linjun Chen & Yingtao Fang

  2. Anhui Artificial Intelligence Laboratory, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, High-tech Zone, Hefei, 230026, Anhui, China

    Yong Wang

  3. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, Guangdong, China

    Cuiyun Gao

  4. College of Computer Science and Technology, Xinjiang Normal University, Urumqi, 830017, Xinjiang, China

    Yong Li

Authors
  1. Yong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Linjun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cuiyun Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yingtao Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.W: Writing - Original Draft, Writing - Review & Editing, Conceptualization, Funding Acquisition, Project Administration, Methodology; L.C: Conceptualization, Data Curation, Investigation, Writing - Original Draft, Writing - Review & Editing, Methodology; C.G: Writing - Review & Editing, Resources; Y.F: Data Curation, Formal Analysis, Validation, ; Y.L: Funding Acquisition, Investigation.

Corresponding author

Correspondence to Linjun Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Chen, L., Gao, C. et al. Prompt enhance API recommendation: visualize the user’s real intention behind this query. Autom Softw Eng 31, 27 (2024). https://doi.org/10.1007/s10515-024-00425-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10515-024-00425-0

Keywords

Search

Navigation