A study on application programming interface recommendation: state-of-the-art techniques, challenges and future directions

Abstract

PurposeThis 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 (API) recommendation system for the developers. Moreover, the current study intends to classify current state-of-the-art techniques supporting automated API recommendations.Design/methodology/approachIn this study, the authors have performed a systematic literature review of studies, which have been published between the years 2004–2021 to achieve the targeted research objective. Subsequently, the authors performed the analysis of 35 primary studies.FindingsThe outcomes of this study are: (1) devising a thematic taxonomy based on the identified developers’ challenges, where mashup-oriented APIs and time-consuming process are frequently encountered challenges by the developers; (2) categorizing current state-of-the-art API recommendation techniques (i.e. clustering techniques, data preprocessing techniques, similarity measurements techniques and ranking techniques); (3) designing a taxonomy based on the identified objectives, where accuracy is the most targeted objective in API recommendation context; (4) identifying a list of evaluation metrics employed to assess the performance of the proposed techniques; (5) performing a SWOT analysis on the selected studies; (6) based on the developer’s challenges, objectives and SWOT analysis, presenting outlines of a recommendation system for the developers and (7) delineating several future research dimensions in API recommendations context.Research limitations/implicationsThis study provides complete guidance to the new researcher in the context of API recommendations. Also, the researcher can target these objectives (accuracy, response time, method recommendation, compatibility, user requirement-based API, automatic service recommendation and API location) in the future. Moreover, the developers can overcome the identified challenges (including mashup-oriented API, Time-consuming process, learn how to use the API, integrated problem, API method usage location and limited usage of code) in the future by proposing a framework or recommendation system. Furthermore, the classification of current state-of-the-art API recommendation techniques also helps the researchers who wish to work in the future in the context of API recommendation.Practical implicationsThis study not only facilitates the researcher but also facilitates the practitioners in several ways. The current study guides the developer in minimizing the development time in terms of selecting relevant APIs rather than following traditional manual selection. Moreover, this study facilitates integrating APIs in a project. Thus, the recommendation system saves the time for developers, and increases their productivity.Originality/valueAPI recommendation remains an active area of research in web and mobile-based applications development. The authors believe that this study acts as a useful tool for the interested researchers and practitioners as it will contribute to the body of knowledge in API recommendations context.

Editorial: Special selection on current bibliometrics and reviews

“…Hot topics include machine learning, large datasets, deep learning and high-level languages. Readers may also be interested in reading recent articles on AI library service innovation (Okunlaya et al , 2022), patterns of AI policies (Wang et al , 2023a), intelligent recommendations (Bokharaei Nia et al , 2023; Nawaz et al , 2023; Senthil Kumaran and Latha, 2023; Shi et al , 2023), library robotic process automation (Basumatary et al , 2023; Chin and Chiu, 2023; Lin et al , 2022; Wójcik, 2023), smart libraries (Khan et al , 2023; Wang, 2023b), AI awareness in libraries (Harisanty et al , 2022; Yoon et al , 2022), AI in library repositories (Nugroho et al , 2023) and ChatGPT (Afjal, 2023; Bin-Hady et al , 2023). Further, readers may be interested in our special section on smart cities (Chang and Abdel-Basset, 2022).…”

Editorial: Special selection on contemporary bibliometric analytics

“…In addition, Nawaz et al (2023) studied the application programming interface recommendation systems using a systematic review of data collected from 2004 to 2021. Further, Shahzad and Khan (2023) investigated the adoption of integrated semantic digital libraries (SDLs) using a systematic literature review.…”