HyOASAM: A Hybrid Open API Selection Approach for Mashup Development

Bo Jiang,Pengxiang Liu,Ye Wang,Yezhi Chen

doi:10.1155/2020/4984375

Abstract

At present, Mashup development has attracted much attention in the field of software engineering. It is the focus of this article to use existing open APIs to meet the needs of Mashup developers. Therefore, how to select the most appropriate open API for a specific user requirement is a crucial problem to be solved. We propose a Hybrid Open API Selection Approach for Mashup development (HyOASAM), which consists of two basic approaches: one is a user-story-driven open API discovery approach, and the other is multidimensional-information-matrix- (MIM-) based open API recommendation approach. The open API discovery approach introduces user stories in agile development to capture Mashup requirements. First, it extracts three components from user stories, and then, it extracts three corresponding properties from open API descriptions. Next, the similarity calculation is performed on two sets of data. The open API recommendation approach first uses MIM to store open APIs, Mashups, and the invoking relationship between them. Second, it enters the matrix obtained in the previous step into a factorization machine model to calculate the association scores between the Mashups and the open APIs, and TOP-N open API lists for creating the Mashup are obtained. Finally, experimental comparison and analysis are carried out on the PWeb dataset. The experimental results show that our approach has improved significantly.

Highlights

Unlike object-oriented software engineering [1, 2], serviceoriented software engineering (SOSE) is used to design, develop, and maintain software systems [3] that use the principles of service-oriented architecture (SOA) [4]
To overcome the above problems, we propose a Hybrid Open API Selection Approach for Mashup development (HyOASAM), which consists of two basic approaches: one is a user-story-driven open API discovery approach, and the other is a multidimensional-information-matrix- (MIM-) based open API recommendation approach. e user-storydriven open API discovery approach is to tackle the first problem
We conducted a series of experiments on user-story-driven open API discovery and MIM-based open API recommendation approach to evaluate the effectiveness of the HyOASAM approach [37]

Summary

Introduction

Unlike object-oriented software engineering [1, 2], serviceoriented software engineering (SOSE) is used to design, develop, and maintain software systems [3] that use the principles of service-oriented architecture (SOA) [4]. All of the above problems make it more and more difficult for developers to select the appropriate high-quality open APIs to build Mashup applications. To overcome the above problems, we propose a Hybrid Open API Selection Approach for Mashup development (HyOASAM), which consists of two basic approaches: one is a user-story-driven open API discovery approach, and the other is a multidimensional-information-matrix- (MIM-) based open API recommendation approach. (2) We propose an approach that breaks the restrictions of open API documents, described by user stories, which can be used to capture and describe Mashup developers’ requirements more accurately and effectively. (3) We tailor the current MIM matrix [26] and introduce factorization machine (FM) into the open API recommendation approach to calculate the semantic similarity more accurately. E rest of the paper is organized as follows: Section 2 introduces previously established open API discovery and recommendation approaches; Section 3 introduces our proposed HyOASAM in detail; Section 4 compares the HyOASAM with other approaches; Section 5 draws the conclusions

Related Work

HyOASAM Approach

Experimental Results and Analysis

Conclusions and Future Work