Труды Института системного программирования РАН (Oct 2018)

Approach to Anti-pattern detection in Service-oriented Software Systems

  • A. S. Yugov

DOI
https://doi.org/10.15514/ISPRAS-2016-28(2)-5
Journal volume & issue
Vol. 28, no. 2
pp. 79 – 96

Abstract

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A service-based approach is a method to develop and integrate program products in a modular manner where each component is available through any net and has the possibility of being dynamically collaborated with other services of the system at run time. That approach is becoming widely adopted in industry of software engineering because it allows the implementation of distributed systems characterized by high quality. Quality attributes can be about the system (e.g., availability, modifiability), business-related aspects (e.g., time to market) or about the architecture (e.g., correctness, consistency). Maintaining quality-attributes on a high level is critical issue because service-based systems lack central control and authority, have limited end-to-end visibility of services, are subject to unpredictable usage scenarios and support dynamic system composition. The constant evolution in systems can easily deteriorate the overall architecture of the system and thus bad design choices, known as anti-patterns, may appear. These are the patterns to be avoided. If we study them and are able to recognize them, then we should be able to avoid them. Knowing bad practices is perhaps as valuable as knowing good practices. With this knowledge, we can re-factor the solution in case we are heading towards an anti-pattern. As with patterns, anti-pattern catalogues are also available. In case of continues evolution of systems, it is metric-based techniques that can be applied to obtain valuable, factual insights into how services work. Given the clear negative impact of anti-patterns, there is a clear and urgent need for techniques and tools to detect them. The article will focus on rules to recognize symptoms of anti-patterns in service-based environment, automated approaches to detection and applying metric-based techniques to this analysis.

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