HyOASAM: A Hybrid Open API Selection Approach for Mashup Development

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.

Ambiguity in user stories: A systematic literature review

“…Finally, user story duplication has been considered initially as a problem of lexical ambiguity. The scarce research on the topic has investigated the similarity of user stories using similarity metrics, particularly the Cosine Similarity [79,80], and Jaccard Index [79] metrics. However, as explained further in sub-Section 5.2.1, those studies concluded that to resolve duplication, it needs to be considered as a semantic ambiguity problem.…”

“…However, it was found that these techniques were not sufficient, and that the problem needed to be addressed at the semantic level of ambiguity. Consequently, it was recommended to calculate similarity taking into account user story dependencies [80]. Also, semantic similarity measures (i.e., the WuP, Lin, and Lesk-A similarity metrics) were recommended for user story duplication detection [79].…”

“…The 17 papers that include a validation or evaluation of the presented solution are classified into the Framework (i.e., [26,28,31,32,41,49,52]), Algorithm (i.e., [42,[78][79][80]), and Model (i.e., [27,44,45,57,68,71]) solution types (see Table 4). In what follows, we review the evidence that is presented of the effectiveness of these solutions.…”

Contexts Enhance Accuracy: On Modeling Context Aware Deep Factorization Machine for Web API QoS Prediction

“…Although ProgrammableWeb, APIs.guru and RapidAPI provide users with search engine for Web API selection, they only use keywords for filtering simply, which cannot meet the needs of users for accurate and high-quality Web API selection efficiently. Furthermore, recent works exploit functional requirements matching to enable finer Web APIs recommendation [10,11], but how to quickly identify and select suitable Web APIs from candidates with similar functionality for ordinary users is still an unsolved problem.…”

Web Augmentation as a Technique to Diminish User Interactions in Repetitive Tasks

“…Linking content from different websites avoids tab switching when locating information from different resources. FaceMashup[2] is an End-User Development environment that empowers users of social networks by supporting them create their own procedures for inspecting and controlling their data [57]. proposes a Hybrid Open API Selection Approach for Mashup development (HyOASAM).…”