Benefits and drawbacks of software reference architectures: A case study

Abstract

Context:\ud Software Reference Architectures (SRAs) play a fundamental role for organizations whose business greatly depends on the efficient development and maintenance of complex software applications. However, little is known about the real value and risks associated with SRAs in industrial practice. \ud Objective:\ud To investigate the current industrial practice of SRAs in a single company from the perspective of different stakeholders. Method An exploratory case study that investigates the benefits and drawbacks perceived by relevant stakeholders in nine SRAs designed by a multinational software consulting company. \ud Results: The study shows the perceptions of different stakeholders regarding the benefits and drawbacks of SRAs (e.g., both SRA designers and users agree that they benefit from reduced development costs; on the contrary, only application builders strongly highlighted the extra learning curve as a drawback associated with mastering SRAs). Furthermore, some of the SRA benefits and drawbacks commonly highlighted in the literature were remarkably not mentioned as a benefit of SRAs (e.g., the use of best practices). Likewise, other aspects arose that are not usually discussed in the literature, such as higher time-to-market for applications when their dependencies on the SRA are managed inappropriately. \ud Conclusions:\ud This study aims to help practitioners and researchers to better understand real SRAs projects and the contexts where these benefits and drawbacks appeared, as well as some SRA improvement strategies. This would contribute to strengthening the evidence regarding SRAs and support practitioners in making better informed decisions about the expected SRA benefits and drawbacks. Furthermore, we make available the instruments used in this study and the anonymized data gathered to motivate others to provide similar evidence to help mature SRA research and practice.Peer ReviewedPostprint (author's final draft

Extending reference architecture of big data systems towards machine learning in edge computing environments

“…Then, the identified elements were mapped into the high level architecture view of the RA [4] (see Figs. 6,7,8,9,10,11,12,13,14,15,16,17,18,19 and 20 for detailed mapping figures). Subsequently, the mapped implementation architectures of individual papers were merged into an updated RA, which included existing [4] and new elements ( Fig.…”

“…RAs are designed for facilitating design of concrete architectures, reducing of risks with proven components, and improvement of communication within an organisation [10]. Real drawbacks and benefits of RAs have been studied with a case study (company), which utilised RAs in multiple projects [10]. RA facilitated development of concrete SW architectures, and reduced maintenance costs.…”

“…In summary, different approaches have been utilised for RA design of big data systems in the literature [10,16] and standardisation [21,22]. Also, new SW processes [25] and platforms [26] have been suggested for facilitating utilisation of ML/AI techniques for the SW/service developers.…”

“…Having an implementation technology independent RA with modularization of system functionalities may be of great help. A RA may facilitate the design of concrete architectures [9], reduce development and maintenance costs of systems [10], facilitate communication between important stakeholders [10], and reduce risks [10]. It may also greatly facilitate communication and shared understanding among the involved design and development process participants with different backgrounds (Product/service/solution business owners, Data scientists, SW engineers and ML engineers).…”

“…In general, the value of RAs has been related to facilitation of concrete system design, reduction of development costs and risks [10], and facilitation of communication between important stakeholders within organisations [10]. In the future, the value of the presented views of the RA should be studied (in more detail) in the development of concrete edge computing systems, which utilise ML techniques in providing services to end users.…”