Mar 1, 2023 · In this paper, we propose a novel automated approach called DI-RAR to retrieve and recommend web API groups for Mashup creation. Specifically, we formulate an automated web API recommendation task as a nondeterministic polynomial problem.

Jul 7, 2022 · To deal with these challenges, this paper designs DAWAR, a diversity-aware Web APIs recommendation approach that finds diversified and compatible APIs for mashup creation.

Aug 18, 2022 · Purpose This study aims to identify the developer’s objectives, current state-of-the-art techniques, challenges and performance evaluation metrics, and presents outlines of a knowledge-based...

Oct 29, 2019 · Web API recommendation technique plays an important role in service-oriented computing and effectively improves the quality of service discovery . A number of research works have been done on Web API recommendation.

Aug 18, 2022 · This study aims to identify the developer’s objectives, current state-of-the-art techniques, challenges and performance evaluation metrics, and presents outlines of a knowledge-based application programming interfaces …

Sep 4, 2024 · API recommendation systems powered by machine learning are transforming how developers find APIs, offering personalized suggestions through collaborative filtering, content-based techniques,...

Mar 17, 2020 · In this paper, we introduce TAPrec, an End-User Development platform that supports the composition of trigger-action rules with dynamic recommendations. By exploiting a hybrid and semantic recommendation algorithm, TAPrec suggests, at composition time, either a) new rules to be used or b) actions for auto-completing a rule.

Feb 24, 2020 · Then, we propose WAR (Web APIs Recommendation), the first data-driven approach for web APIs recommendation that integrates API discovery, verification and selection operations based on...

Mar 1, 2023 · In this paper, we propose a novel automated approach called DI-RAR to retrieve and recommend web API groups for Mashup creation. Specifically, we formulate an automated web API recommendation task as a nondeterministic polynomial problem.

This research introduces a novel approach by integrating temporal dynamics into web API recommendations. Utilizing Long Short-Term Memory (LSTM) for time series forecasting, this model goes beyond static awareness, providing developers with forward-looking insights.