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Record W4403037467 · doi:10.9734/ajrcos/2024/v17i10507

DevOps Implementation: Essential Tools, Best Practices, and Solutions to Overcome Challenges for Seamless Development and Operations Integration

2024· article· en· W4403037467 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAsian Journal of Research in Computer Science · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsDevOpsComputer scienceProcess managementSoftware engineeringBest practiceEngineering managementSystems engineeringEngineeringSoftware deploymentPolitical science

Abstract

fetched live from OpenAlex

Aims: This study aims to explore the core principles, tools, and best practices for implementing DevOps, emphasizing its benefits, such as enhanced efficiency, better collaboration, and faster time-to-market. It also seeks to identify common challenges in DevOps adoption and provide practical solutions. Study Design: A qualitative research approach was used, incorporating case studies, industry reports, and expert interviews to analyze DevOps implementation across various industries. Place and Duration of Study: Conducted across organizations in technology, finance, and healthcare sectors from 2022 to 2023 [1,2]. Methodology: The research utilized both primary and secondary data. Primary data were collected through interviews with DevOps experts and consultants, while secondary data included published case studies, industry white papers, and academic research. This comprehensive analysis identified recurring themes, challenges, and effective strategies in DevOps adoption. The study also examined successful case studies to illustrate best practices and drew insights from experts to address barriers and propose actionable recommendations. Results: The analysis highlighted that strong leadership support (35%), continuous learning (30%), and effective communication (20%) are critical for successful DevOps implementation. Organizations that invested in automation tools such as Jenkins, Docker, Kubernetes, and GitLab experienced significant gains in workflow efficiency, continuous integration, and delivery. Cultural resistance (40%) and lack of expertise (25%) were the main barriers to DevOps adoption. A positive relationship was noted between cultural change initiatives and successful DevOps implementation (R = 0.85). Conclusion: Effective DevOps adoption requires a cultural shift towards collaboration, shared responsibility, and continuous improvement, backed by strong leadership and strategic automation. To successfully implement DevOps, organizations should focus on cultivating a collaborative culture, ensuring leadership commitment, and investing in continuous learning and automation tools to overcome challenges and achieve their objectives.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.005
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.405
GPT teacher head0.482
Teacher spread0.077 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it