Data Correction and Evolution Analysis of the ProgrammableWeb Service Ecosystem

TL;DR

This paper investigates the quality issues in the ProgrammableWeb dataset, proposes a method to correct data by estimating API and mashup life cycles, and analyzes ecosystem evolution using dynamic network models.

Contribution

It introduces a novel data correction method for the ProgrammableWeb dataset and applies dynamic network models to analyze service ecosystem evolution.

Findings

Identified significant quality issues in the ProgrammableWeb dataset.

Proposed a new approach to estimate API and mashup life cycles.

Demonstrated the use of three dynamic network models for ecosystem analysis.

Abstract

The evolution analysis on Web service ecosystems has become a critical problem as the frequency of service changes on the Internet increases rapidly. Developers need to understand these evolution patterns to assist in their decision-making on service selection. ProgrammableWeb is a popular Web service ecosystem on which several evolution analyses have been conducted in the literature. However, the existing studies have ignored the quality issues of the ProgrammableWeb dataset and the issue of service obsolescence. In this study, we first report the quality issues identified in the ProgrammableWeb dataset from our empirical study. Then, we propose a novel method to correct the relevant evolution analysis data by estimating the life cycle of application programming interfaces (APIs) and mashups. We also reveal how to use three different dynamic network models in the service ecosystem evolution analysis based on the corrected ProgrammableWeb dataset. Our experimental experience iterates the quality issues of the original ProgrammableWeb and highlights several research opportunities.