Abstract
With the ever-increasing prosperity of web Application Programming Interface (API) sharing platforms, it is becoming an economic and efficient way for software developers to design their interested mashups through web API re-use. Generally, a software developer can browse, evaluate, and select his or her preferred web APIs from the API's sharing platforms to create various mashups with rich functionality. The big volume of candidate APIs places a heavy burden on software developers’ API selection decisions. This, in turn, calls for the support of intelligent API recommender systems. However, existing API recommender systems often face two challenges. First, they focus more on the functional accuracy of APIs while neglecting the APIs’ actual compatibility. This then creates incompatible mashups. Second, they often require software developers to input a set of keywords that can accurately describe the expected functions of the mashup to be developed. This second challenge tests partial developers who have little background knowledge in the fields. To tackle the above-mentioned challenges, in this article we propose a compatibility-aware and text description-driven web API recommendation approach (named WARtext). WARtext guarantees the compatibility among the recommended APIs by utilizing the APIs’ composition records produced by historical mashup creations. Besides, WARtext entitles a software developer to type a simple text document that describes the expected mashup functions as input. Then through textual description mining, WARtext can precisely capture the developers’ functional requirements and then return a set of APIs with the highest compatibility. Finally, through a real-world mashup dataset ProgrammableWeb, we validate the feasibility of our novel approach.
- K. Ota, M. S. Dao, V. Mezaris, and F. G. B. D. Natale. 2017. Deep learning for mobile multimedia: A survey. ACM Transactions on Multimedia Computing, Communications, and Applications 13, 3s (2017), 34. Google Scholar
Digital Library
- S. Ding, S. Qu, Y. Xi, and S. Wan. 2020. Stimulus-driven and concept-driven analysis for image caption generation. Neurocomputing 398 (2020), 520–530.Google Scholar
Cross Ref
- C. F. Hsu, C. L. Fan, T. H. Tsai, C. Y. Huang, C. H. Hsu, and K. T. Chen. 2016. Toward an adaptive screencast platform: Measurement and optimization. ACM Transactions on Multimedia Computing, Communications, and Applications 12, 5s (2016), 79. Google Scholar
Digital Library
- W. Tan, Y. Fan, A. Ghoneim, M. A. Hossain, and S. Dustdar. 2016. From the service-oriented architecture to the web API economy. IEEE Internet Computing 20, 4 (2016), 64–68.Google Scholar
Digital Library
- M. Bahrami and W. Chen. WATAPI: Composing web API specification from API documentations through an intelligent and interactive annotation tool. In Proc. of IEEE International Conference on Big Data (Big Data’19). IEEE Press, San Francisco, CA, 4573–4578.Google Scholar
- S. Liu, Y. Li, G. Sun, B. Fan, and S. Deng. Hierarchical RNN networks for structured semantic web API model learning and extraction. IEEE International Conference on Web Services (ICWS’17), IEEE Press, San Francisco, CA, 708–713.Google Scholar
- F. Marcantoni, M. Diamantaris, S. Ioannidis, and J. Polakis. A large-scale study on the risks of the html5 webapi for mobile sensor-based attacks. In Proc. of World Wide Web Conference (WWW’19), ACM Press, New York, NY, 3063–3071. Google Scholar
Digital Library
- Retrieved on February 29, 2020, from https://www.programmableweb.com.Google Scholar
- B. Cao, X. F. Liu, M. M. Rahman, B. Li, J. Liu, and M. Tang. 2020. Integrated content and network-based service clustering and web APIs recommendation for mashup development. IEEE Transactions on Services Computing 13, 1 (2020), 99–113.Google Scholar
Cross Ref
- B. Cheng, Z. Zhai, S. Zhao, and J. Chen. 2017. LSMP: A lightweight service mashup platform for ordinary users. IEEE Communications Magazine 55, 4 (2017), 116–123. Google Scholar
Digital Library
- L. Yao, X. Wang, Q. Z. Sheng, B. Benatallah, and C. Huang. 2020. Mashup recommendation by regularizing matrix factorization with API co-invocations. IEEE Transactions on Services Computing 2020. DOI:10.1109/TSC.2018.2803171Google Scholar
- M. Alshangiti, W. Shi, X. Liu, and Q. Yu. 2020. A Bayesian learning model for design-phase service mashup popularity prediction. Expert Systems with Applications 149 (2020). https://doi.org/10.1016/j.eswa.2020.113231.Google Scholar
- B. Cao, J. Liu, Y. Wen, H. Li, Q. Xiao, and J. Chen. 2019. QoS-aware service recommendation based on relational topic model and factorization machines for IoT mashup applications. Journal of Parallel and Distributed Computing 132 (2019), 177–189.Google Scholar
Digital Library
- M. Shi, Y. Tang, and J. Liu. 2019. Functional and contextual attention-based LSTM for service recommendation in mashup creation. IEEE Transactions on Parallel and Distributed Systems 30, 5 (2019), 1077–1090.Google Scholar
Cross Ref
- N. Almarimi, A. Ouni, S. Bouktif, M. W. Mkaouer, R. G. Kula, and M. A. Saied. 2019. Web service API recommendation for automated mashup creation using multi-objective evolutionary search. Applied Soft Computing 85 (2019), 105830.Google Scholar
Digital Library
- W. Pan and C. Chai. 2018. Structure-aware mashup service clustering for cloud-based Internet of Things using genetic algorithm based clustering algorithm. Future Generation Computer Systems 87 (2018), 267–277.Google Scholar
Digital Library
- T. Liang, L, Chen, J. Wu, and A. Bouguettaya. 2016. Exploiting heterogeneous information for tag recommendation in API management. In Proc. of 23rd International Conference on Web Services (ICWS’16). IEEE Press, San Francisco, CA, 436–443.Google Scholar
- Y. Zhong, Y. Fan, W. Tan, and J. Zhang. 2018. Web service recommendation with reconstructed profile from mashup descriptions. IEEE Transactions on Automation Science and Engineering 15, 2 (2018), 468–478.Google Scholar
Cross Ref
- G. Huang, Y. Ma, X. Liu, Y. Luo, X. Lu, and M. Brian Blake. 2015. Model-based automated navigation and composition of complex service mashups. IEEE Transactions on Services Computing 8, 3 (2015), 494–506.Google Scholar
Cross Ref
- N. Chen, N. Cardozo, and S. Clarke. 2018. Goal-driven service composition in mobile and pervasive computing. IEEE Transactions on Services Computing 11, 1 (2018), 49–62.Google Scholar
Cross Ref
- Q. He, J. Yan, H. Jin, and Y. Yang. 2014. Quality-aware service selection for service-based systems based on iterative multi-attribute combinatorial auction. IEEE Transactions on Software Engineering 40, 2 (2014), 192–215. Google Scholar
Digital Library
- W. Gao and J. Wu. 2017. A novel framework for service set recommendation in mashup creation. In Proc. of IEEE International Conference on Web Services (ICWS’17). IEEE Press, Honolulu, HI, 65–72.Google Scholar
- Q. Gu, J. Cao, and Q. Peng. 2016. Service package recommendation for mashup creation via mashup textual description mining. In Proc. of IEEE International Conference on Web Services (ICWS’16). IEEE Press, Francisco, CA, 452–459.Google Scholar
- F. Michel, C. Faron-Zucker, O. Corby et al. 2019. Enabling automatic discovery and querying of web APIs at web scale using linked data standards. In Proceedings of 2019 World Wide Web Conference (WWW’19). ACM Press, New York, NY, 883–892. Google Scholar
Digital Library
- L. Qi, Q. He, F. Chen, X. Zhang, W. Dou, and Q. Ni. 2020. Data-driven web APIs recommendation for building web applications. IEEE Transactions on Big Data 2020. DOI:10.1109/TBDATA.2020.2975587Google Scholar
- L. Qi, Q. He, F. Chen, W. Dou, S. Wan, X. Zhang, and X. Xu. 2019. Finding all you need: Web APIs recommendation in web of things through keywords search. IEEE Transactions on Computational Social Systems 6, 5 (2019), 1063–1072.Google Scholar
Cross Ref
- Retrieved February 10, 2020, from https://fasttext.cc/.Google Scholar
- H. Liu, H. Kou, C. Yan, and L. Qi. 2020. Keywords-driven and popularity-aware paper recommendation based on undirected paper citation graph. Complexity 2020, Article 2085638 (2020), 15.Google Scholar
- X. Zhou, W. Liang, K. Wang, R. Huang, and Q. Jin. 2018. Academic influence aware and multidimensional network analysis for research collaboration navigation based on scholarly big data. IEEE Transactions on Emerging Topics in Computing 2018. DOI:10.1109/TETC.2018.2860051Google Scholar
- Z. Gao, Y. Li, and S. Wan. 2020. Exploring deep learning for view-based 3D model retrieval. ACM Transactions on Multimedia Computing, Communications, and Applications 2020. Google Scholar
Digital Library
- X. Zhou, Y. Li, and W. Liang. 2020. CNN-RNN based intelligent recommendation for online medical pre-diagnosis support. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020. DOI:10.1109/TCBB.2020.2994780Google Scholar
Digital Library
- S. Wan, Y. Xia, L. Qi, Y. H. Yang, and M. Atiquzzaman. 2020. Automated colorization of a grayscale image with seed points propagation. IEEE Transactions on Multimedia 2020.Google Scholar
- C. Zhou, A. Li, A. Hou, Z. Zhang, Z. Zhang, and F. Wang. 2020. Modeling methodology for early warning of chronic heart failure based on real medical big data. Expert Systems with Applications 2020. DOI:10.1016/j.eswa.2020.113361Google Scholar
- Z. Gao, H. Xue, and S. Wan. 2020. Multiple discrimination and pairwise CNN for view-based 3D object retrieval. Neural Networks 2020.Google Scholar
- L. Wang, X. Zhang, R. Wang, C. Yan, H. Kou, and L. Qi. 2020. Diversified service recommendation with high accuracy and efficiency. Knowledge-Based Systems 2020.Google Scholar
- J. Li, T. Cai, K. Deng, X. Wang, T. Sellis, and F. Xia. 2020. Community-diversified influence maximization in social networks. Information Systems 92 (2020), 1–12.Google Scholar
Cross Ref
- H. Liu, H. Kou, C. Yan, and L. Qi. 2019. Link prediction in paper citation network to construct paper correlated graph. EURASIP Journal on Wireless Communications and Networking (2019), Article number 233.Google Scholar
- T. Cai, J. Li, A. S. Mian, R. Li, T. Sellis, and J. X. Yu. 2020. Target-aware holistic influence maximization in spatial social networks. IEEE Transactions on Knowledge and Data Engineering 2020. DOI:10.1109/TKDE.2020.3003047Google Scholar
- X. Zhou, W. Liang, K. Wang, H. Wang, L. T. Yang, and Q. Jin. 2020. Deep learning enhanced human activity recognition for internet of healthcare things. IEEE Internet of Things Journal 2020. DOI:10.1109/JIOT.2020.2985082Google Scholar
- W. Zhong, X. Yin, X. Zhang, S. Li, W. Dou, R. Wang, and L. Qi. 2020. Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment. Computer Communications 2020. DOI:10.1016/j.comcom.2020.04.018Google Scholar
- Z. Gao, H. Xuan, H. Zhang, S. Wan, and K. K. R. Choo. 2019. Adaptive fusion and category-level dictionary learning model for multiview human action recognition. IEEE Internet of Things Journal 6, 6 (2019), 9280–9293.Google Scholar
Cross Ref
- X. Chi, C. Yan, H. Wang, W. Rafique, and L. Qi. 2020. Amplified LSH-based recommender systems with privacy protection. Concurrency and Computation: Practice and Experience 2020. DOI:10.1002/CPE.5681Google Scholar
Index Terms
Compatibility-Aware Web API Recommendation for Mashup Creation via Textual Description Mining
Recommendations
DAWAR: Diversity-aware Web APIs Recommendation for Mashup Creation based on Correlation Graph
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalWith the ever-increasing popularity of microservice architecture, a considerable number of enterprises or organizations have encapsulated their complex business services into various lightweight functions as published them accessible APIs (Application ...
Web API service recommendation for Mashup creation
Mashup refers to a sort of web application developed by reusing or combining web API services, which are very popular software components for building distributed applications. As the number of open web APIs increases, to find suitable web APIs for Mashup ...
Aggregating Functionality, Use History, and Popularity of APIs to Recommend Mashup Creation
Service-Oriented ComputingAbstractCreating mashups from existing Web APIs has provided an effective means to boost software reuse and approach the full potential of online programming resources. One of the key hindrance faced by mashup creation is to discover relevant APIs, ...






Comments