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10.1109/ICSM.2015.7332473guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
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Exploring API method parameter recommendations

Published: 29 September 2015 Publication History

Abstract

A number of techniques have been developed that support method call completion. However, there has been little research on the problem of method parameter completion. In this paper, we first present a study that helps us to understand how developers complete method parameters. Based on our observations, we developed a recommendation technique, called Parc, that collects parameter usage context using a source code localness property that suggests that developers tend to collocate related code fragments. Parc uses previous code examples together with contextual and static type analysis to recommend method parameters. Evaluating our technique against the only available state-of-the-art tool using a number of subject systems and different Java libraries shows that our approach has potential. We also explore the parameter recommendation support provided by the Eclipse Java Development Tools (JDT). Finally, we discuss limitations of our proposed technique and outline future research directions.

Cited By

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  • (2023)Let's Chat to Find the APIs: Connecting Human, LLM and Knowledge Graph through AI ChainProceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE56229.2023.00075(471-483)Online publication date: 11-Nov-2023
  • (2021)A Systematic Review of API Evolution LiteratureACM Computing Surveys10.1145/347013354:8(1-36)Online publication date: 4-Oct-2021
  • (2021)Understanding Software-2.0ACM Transactions on Software Engineering and Methodology10.1145/345347830:4(1-42)Online publication date: 23-Jul-2021
  • Show More Cited By

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Published In

cover image Guide Proceedings
ICSME '15: Proceedings of the 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME)
September 2015
611 pages
ISBN:9781467375320

Publisher

IEEE Computer Society

United States

Publication History

Published: 29 September 2015

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Cited By

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  • (2023)Let's Chat to Find the APIs: Connecting Human, LLM and Knowledge Graph through AI ChainProceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE56229.2023.00075(471-483)Online publication date: 11-Nov-2023
  • (2021)A Systematic Review of API Evolution LiteratureACM Computing Surveys10.1145/347013354:8(1-36)Online publication date: 4-Oct-2021
  • (2021)Understanding Software-2.0ACM Transactions on Software Engineering and Methodology10.1145/345347830:4(1-42)Online publication date: 23-Jul-2021
  • (2020)API method recommendation via explicit matching of functionality verb phrasesProceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3368089.3409731(1015-1026)Online publication date: 8-Nov-2020
  • (2019)Automated API-usage update for Android appsProceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3293882.3330571(204-215)Online publication date: 10-Jul-2019
  • (2018)Effective API recommendation without historical software repositoriesProceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering10.1145/3238147.3238216(282-292)Online publication date: 3-Sep-2018
  • (2018)Exploring the use of automated API migrating techniques in practiceProceedings of the 15th International Conference on Mining Software Repositories10.1145/3196398.3196420(503-514)Online publication date: 28-May-2018
  • (2017)Learning syntactic program transformations from examplesProceedings of the 39th International Conference on Software Engineering10.1109/ICSE.2017.44(404-415)Online publication date: 20-May-2017
  • (2016)API recommendation system for software developmentProceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering10.1145/2970276.2975940(896-899)Online publication date: 25-Aug-2016
  • (2016)API code recommendation using statistical learning from fine-grained changesProceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering10.1145/2950290.2950333(511-522)Online publication date: 1-Nov-2016

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