Toward the Health Measure for Open Source Software Ecosystem Via Projection Pursuit and Real-Coded Accelerated Genetic

Abstract

The benign development of Open-source Software Ecosystem (or OSSE) helps to fuse the wisdom of the community. It can facilitate the development and solve the urgent application needs of large-scale complex software systems. To guarantee that an OSSE is stable and effective for supporting the application development, health assessment for an OSSE has become a research hotspot. In this paper, starting from a new perspective, the OSSE is compared with the ecosystem in the natural world. An OSSE health measure method is proposed by integrating projection pursuit and real-coded accelerated genetic algorithm. First, according to the snowball sampling data collection method and the grounded theory, the data is collected and processed. Second, by designing evaluation indicators and utility functions, the projection pursuit classification model of the natural ecosystem is evaluated and combined with a real-coded accelerated genetic algorithm, thereby designing the health measure model. The experimental results suggest the effectiveness of the proposed approach.

Keywords

• Open-source software ecosystem
• health measure
• projection pursuit model
• real-coded accelerated genetic algorithm