Modularity is one of the four key principles of software design and architecture. According to this principle, software should be organized into modules that are tightly linked internally (high cohesion), whereas at the same time as independent from other modules as possible (low coupling). However, in practice, this principle is violated due to poor architecting design decisions, lack of time, or coding shortcuts, leading to a phenomenon termed as architectural technical debt (ATD). To alleviate this problem (lack of architectural modularity), the most common solution is the application of a software refactoring, namely Move Class-i.e., moving classes (the core artifact in object-oriented systems) from one module to another. To identify Move Class refactoring opportunities, we employ a search-based optimization process, relying on optimization metrics, through which optimal moves are derived. Given the extensive search space required for applying a brute-force search strategy, in this paper, we propose the use of a genetic algorithm that rearranges existing software classes into existing or new modules (software packages in Java, or folders in C++). To validate the usefulness of the proposed refactorings, we performed an industrial case study on three projects (from the Aviation, Healthcare, and Manufacturing application domains). The results of the study indicate that the proposed architecture reconstruction is able to improve modularity, improving both coupling and cohesion. The obtained results can be useful to practitioners through an open source tool; whereas at the same point, they open interesting future work directions.
Developing software based on services is one of the most emerging programming paradigms in software development. Service-based software development relies on the composition of services (i.e., pieces of code already built and deployed in the cloud) through orchestrated API calls. Black-box reuse can play a prominent role when using this programming paradigm, in the sense that identifying and reusing already existing/deployed services can save substantial development effort. According to the literature, identifying reusable assets (i.e., components, classes, or services) is more successful and efficient when the discovery process is domain-specific. To facilitate domain-specific service discovery, we propose a service classification approach that can categorize services to an application domain, given only the service description. To validate the accuracy of our classification approach, we have trained a machine-learning model on thousands of open-source services and tested it on 67 services developed within two companies employing servicebased software development. The study results suggest that the classification algorithm can perform adequately in a test set that does not overlap with the training set; thus, being (with some confidence) transferable to other industrial cases. Additionally, we expand the body of knowledge on software categorization by highlighting sets of domains that consist 'grey-zones' in service classification.
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