Architecture Knowledge for Evaluating Scalable Databases

Gorton, Ian; Klein, John P.; Nurgaliev, Albert

doi:10.21236/ada614251

Cited by 7 publications

(3 citation statements)

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“…Decision documentation models have been used by many authors before, and quite a number of them are focused on services, such as those on serviceoriented solutions [29], service-based platform integration [14], REST vs. SOAP [19], microservice API quality [26], big data repositories [7], and service discovery and fault tolerance [9]; however, none of them considers service meshes yet.…”

Section: Related Workmentioning

confidence: 99%

Guiding Architectural Decision Making on Service Mesh Based Microservice Architectures

Malki

Zdun

2019

Software Architecture

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Microservices are becoming the de-facto standard way for software development in the cloud and in service-oriented computing. Service meshes have been introduced as a dedicated infrastructure for managing a network of containerized microservices, in order to cope with the complexity, manageability, and interoperability challenges in especially large-scale microservice architectures. Unfortunately so far no dedicated architecture guidance for designing microservices and choosing among technology options in a service mesh exist. As a result, there is a substantial uncertainty in designing and using microservices in a service mesh environment today. To alleviate this problem, we have performed a model-based qualitative in-depth study of existing practices in this field in which we have systematically and in-depth studied 40 reports of established practices from practitioners. In our study we modeled our findings in a rigorously specified reusable architectural decision model, in which we identified 14 architectural design decisions with 47 decision outcomes and 77 decision drivers in total. We estimated the uncertainty in the resulting design space with and without use of our model, and found that a substantial uncertainty reduction can be potentially achieved by applying our model.

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Section: Related Workmentioning

confidence: 99%

Guiding Architectural Decision Making on Service Mesh Based Microservice Architectures

Malki

Zdun

2019

Software Architecture

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“…for service-oriented solutions [21], service-based platform integration [13], REST vs. SOAP [16], and big data repositories [7]). However, this kind of research does not yet encompass microservice architectures, apart from our own prior study on microservice API quality [19].…”

Section: Related Workmentioning

confidence: 99%

Supporting Architectural Decision Making on Data Management in Microservice Architectures

Ntentos

Zdun

Plakidas

et al. 2019

Software Architecture

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Today many service-based systems follow the microservice architecture style. As microservices are used to build distributed systems and promote architecture properties such as independent service development, polyglot technology stacks including polyglot persistence, and loosely coupled dependencies, architecting data management is crucial in most microservice architectures. Many patterns and practices for microservice data management architectures have been proposed, but are today mainly informally discussed in the so-called "grey literature": practitioner blogs, experience reports, and system documentations. As a result, the architectural knowledge is scattered across many knowledge sources that are usually based on personal experiences, inconsistent, and, when studied on their own, incomplete. In this paper we report on a qualitative, in-depth study of 35 practitioner descriptions of best practices and patterns on microservice data management architectures. Following a model-based qualitative research method, we derived a formal architecture decision model containing 325 elements and relations. Comparing the completeness of our model with an existing pattern catalog, we conclude that our architectural decision model substantially reduces the effort needed to sufficiently understand microservice data management decisions, as well as the uncertainty in the design process.

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“…These improvements are to ensure the availability for mobility and utilization of SAP in future projects. 8 The five different SAP activities are shown in Figure 1, each activity has specific tasks and participants. Table 1 describes the aforementioned steps in a comprehensive way.…”

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confidence: 99%

Mining software architecture knowledge: Classifying stack overflow posts using machine learning

Ali

Mushtaq

Rasheed

et al. 2021

Concurrency and Computation

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Software Architectural Process (SAP) is a core and excessively knowledge intensive phase of software development life cycle, as it consumes and produces knowledge artifacts, simultaneously. SAP is about making design decisions, and the changes in these verdicts may pose adverse effects on software projects. The performance and properties of software components are fundamentally influenced by the design decisions.The implementation of immature and abrupt design decisions seriously threatens the development process of SAP. Moreover, software architectural knowledge management (AKM) approaches offer systematic ways to support SAP through versatile architectural solutions and design decisions. However, the majority of software organizations have limited access to data and still depend upon manually created and maintained AKM process. In this paper, we have utilized the one of the most prominent online community for software development (i.e., Stack Overflow) as a source of SAP knowledge to support AKM. In order to support AKM, we have proposed a supervised machine learning-based approach to classify the architectural knowledge into predefined categories, that is, analysis, synthesis, evaluation, and implementation. We have employed different combinations of feature selection technique to achieve the optimal classification results of the used classifiers (Support Vector Machine [SVM], K-Nearest Neighbor, Random Forest, and Naive Bayes [NB]). Among these classifiers, SVM with Uni-gram feature set provides best classification results and attains 85.80% accuracy.For evaluating the proposed approach's effectiveness, we have also computed the suitability of the classifiers, that is, the cost of computation along with its accuracy, and NB with Uni-gram feature set proved to be the most suitable. K E Y W O R D Sarchitectural knowledge management, stack overflow, crowd-sourced communities, text mining, classification INTRODUCTIONSoftware architectural design appears to have a significant importance in Software Development Life Cycle (SDLC). Its manifests are the early design decisions which help to determine the system development, deployment, and evaluation. To develop a quality project, better architectural decisions are essential and these decisions pose vast challenges for software engineers. Architectural Knowledge Management (AKM) is used to capture

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Architecture Knowledge for Evaluating Scalable Databases

Cited by 7 publications

References 20 publications

Guiding Architectural Decision Making on Service Mesh Based Microservice Architectures

Guiding Architectural Decision Making on Service Mesh Based Microservice Architectures

Supporting Architectural Decision Making on Data Management in Microservice Architectures

Mining software architecture knowledge: Classifying stack overflow posts using machine learning

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