Data Correction and Evolution Analysis of the ProgrammableWeb Service Ecosystem
This work addresses data quality and evolution patterns for developers in web service ecosystems, but it is incremental as it builds on existing studies by correcting known dataset issues.
The study tackled the problem of quality issues and service obsolescence in the ProgrammableWeb dataset, proposing a novel method to correct evolution analysis data by estimating API and mashup life cycles, and revealing how to use dynamic network models for analysis.
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.