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The relevance of application domains in empirical findings

The term 'software ecosystem' refers to a collection of software systems that are related in some way. Researchers have been using different levels of aggregation to define an ecosystem: grouping them by a common named project (e.g., the Apache ecosystem); or considering all the projects contained in online repositories (e.g., the GoogleCode ecosystem). In this paper we propose a definition of ecosystem based on application domains: software systems are in the same ecosystem if they share the same application domain, as described by a similar technological scope, context or objective. As an example, all projects implementing networking capabilities to trade Bitcoin and other virtual currencies can be considered as part of the same "cryptocurrency" ecosystem. Utilising a sample of 100 Java software systems, we derive their application domains using the Latent Dirichlet Allocation (LDA) approach. We then evaluate a suite of object-oriented metrics per ecosystem, and test a null hypothesis: 'the OO metrics of all ecosystems come from the same population'. Our results show that the null hypothesis is rejected for most of the metrics chosen: the ecosystems that we extracted, based on application domains, show different structural properties. From the point of view of the interested stakeholders, this could mean that the health of a software system depends on domain-dependent factors, that could be common to the projects in the same domain-based ecosystem.