ResearchOps: The case for DevOps in scientific applications

Bayser, Maximilien de; Azevedo, Leonardo Guerreiro; Cerqueira, Renato

doi:10.1109/inm.2015.7140503

Cited by 34 publications

(24 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Velocity is of paramount importance in modern data-intensive systems, especially in the context of real time analytics, where results are delivered in a continuous fashion and results are only useful if the delay is very short. However, before focusing on testing for velocity, the research community is still struggling with the uncertainty of the output of test cases and the definition of suitable oracles ( [3]) and the volume and variety of data that data-intensive systems can potentially handle (e.g., [26], [12]). A certain number of papers concern testing the correctness and the integrity of the data stored in databases by data-intensive systems.…”

Section: Discussionmentioning

confidence: 99%

On Testing Data-Intensive Software Systems

Felderer

Russo

2019

Security and Quality in Cyber-Physical Systems Engineering

View full text Add to dashboard Cite

Today's software systems like cyber-physical production systems or big data systems have to process large volumes and diverse types of data which heavily influences the quality of these so-called data-intensive systems. However, traditional software testing approaches rather focus on functional behavior than on data aspects. Therefore, the role of data in testing has to be rethought and specific testing approaches for data-intensive software systems are required. Thus, the aim of this chapter is to contribute to this area by (1) providing basic terminology and background on data-intensive software systems and their testing, and (2) presenting the state of the research and the hot topics in the area. Finally, the directions of research and the new frontiers on testing data-intensive software systems are discussed.

show abstract

Section: Discussionmentioning

confidence: 99%

On Testing Data-Intensive Software Systems

Felderer

Russo

2019

Security and Quality in Cyber-Physical Systems Engineering

View full text Add to dashboard Cite

show abstract

“…Five primary studies have presented the principles of DevOps with the focus on teamwork, communication, and collaboration between development and operations. ()…”

Section: Resultsmentioning

confidence: 99%

“…Break down barriers between development and operations by enabling trust, sharing tools and supporting communication, collaboration, and integration. () The focus is on teamwork, communication, and collaboration rather than tools and processes, eg, maximize team performance. () Pr2: automation.…”

Section: Resultsmentioning

confidence: 99%

Towards a benefits dependency network for DevOps based on a systematic literature review

Jabbari

Ali

Petersen

et al. 2018

J Software Evolu Process

View full text Add to dashboard Cite

DevOps as a new way of thinking for software development and operations has received much attention in the industry, while it has not been thoroughly investigated in academia yet. The objective of this study is to characterize DevOps by exploring its central components in terms of principles, practices and their relations to the principles, challenges of DevOps adoption, and benefits reported in the peer-reviewed literature. As a key objective, we also aim to realize the relations between DevOps practices and benefits in a systematic manner. A systematic literature review was conducted. Also, we used the concept of benefits dependency network to synthesize the findings, in particular, to specify dependencies between DevOps practices and link the practices to benefits. We found that in many cases, DevOps characteristics, ie, principles, practices, benefits, and challenges, were not sufficiently defined in detail in the peer-reviewed literature.In addition, only a few empirical studies are available, which can be attributed to the nascency of DevOps research. Also, an initial version of the DevOps benefits dependency network has been derived. The definition of DevOps principles and practices should be emphasized given the novelty of the concept. Further empirical studies are needed to improve the benefits dependency network presented in this study. KEYWORDSbenefits and values, challenges, development and operations, DevOps, principles and practices, systematic literature review

show abstract

“…• DevOps was developed to address the disjoint between development and deployment (De Bayser, Azevedo & Cerqueira 2015;Fitzgerald & Stol 2017;Virmani 2015). DevOps was created to meet the needs of large organisations which are developing large, complex ICT solutions (Fitzgerald & Stol 2017).…”

Section: Project Methodsmentioning

confidence: 99%

Conceptualising a multidimensional model of information communication and technology project complexity

Joseph

2017

S. Afr. j. inf. manag.

View full text Add to dashboard Cite

Background: Information communication and technology (ICT) projects are different from other projects, such as construction, and require a new perspective to determine their true nature. The lacklustre state of ICT projects has plagued researchers and practitioners for decades as they are yet to understand why ICT projects do not perform. Literature places significant emphasis on success criteria and success factors for determining project success, but this is a unilateral view as the level of complexity involved is underestimated. ICT projects, however, are multifaceted as there are a number of dimensions that influence the management and outcome of a project.Objectives: This article aimed to illuminate how the dimensions are interdependent and interconnected through the construction of a conceptual model of ICT project complexity.Methods: Content analysis was used to identify and understand the various dimensions and facilitated construction of the model.Results: The article identified five dimensions that affect ICT projects, viz. project success, project lifecycle, project complexity, project types and project methods. Each dimension was analysed to understand the key constructs and elements that need to be considered. The dimensions were mapped in a multidimensional model.Conclusion: The multidimensional model of ICT project complexity can be used by ICT project managers to more effectively manage projects as they are provided with a greater understanding of ICT project influences.

show abstract

ResearchOps: The case for DevOps in scientific applications

Cited by 34 publications

References 12 publications

On Testing Data-Intensive Software Systems

On Testing Data-Intensive Software Systems

Towards a benefits dependency network for DevOps based on a systematic literature review

Conceptualising a multidimensional model of information communication and technology project complexity

Contact Info

Product

Resources

About