CoChat
Sign Up
Cited by 37Data Quality and ManagementSoftware Engineering Techniques and PracticesMulti-Criteria Decision Making

Multicriteria Based Decision Making of DevOps Data Quality Assessment Challenges Using Fuzzy TOPSIS

Open access
Rafi, Saima, Yu, Wu, Akbar, Muhammad Azeem, Alsanad, Ahmed, Gumaei, Abdu·2020·IEEE Access
DOI 10.1109/access.2020.2976803
Open this paper in CoChat — free accountOpen PDF

Abstract

In current era, DevOps gain much interaction in software industry as it provides the flexible development environment. To meet the continuous development and operations, DevOps mainly focus, to integrate the data from heterogeneous source. While DevOps adoption, the quality assessment of data integrated from heterogeneous environment, is important and challenging at the same time. This study aims to identify the critical factors that could negatively impact the data quality assessment process in DevOps. We have used the systematic literature review (SLR) approach and identify a total of 13 critical challenging factors. The finding of SLR are further validated with industry experts via questionnaire survey. Finally, we have applied the Fuzzy TOPSIS approach to prioritize the investigated challenging factors with respect to their significance of DevOps data quality assessment process. The results show that analyzing data in real time, visualization of data and missing information and other invalid data are the highest ranked challenging factors which need to be addressed on priority basis, to successfully measure the quality of heterogeneous data in DevOps. We believe that the finding of this study will assist the practitioner to consider the most significant factors for measuring the quality of heterogeneous data in DevOps.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/access.2020.2976803

Cites

61 of 62 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Lack of automated testing tool performance measures for security is a DevOps security challenge.

    “We have also identified lack of automated testing tool performance measures for security [2], [5], [20], [23], [24] as a critical challenge that have a negative impact on DevOps security.”

    From Prioritization based taxonomy of DevOps security challenges using PROMETHEE (2020) · cites this paper as [20]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 6

  1. A. Zaveri, A. Rula, A. Maurino, R. Pietrobon, J. Lehmann, and S. Auer, ''Quality assessment for linked data: A survey,'' Semantic Web, vol. 7, no. 1, pp. 63-93, Mar. 2015.
  2. M. Lewis-Beck, A. E. Bryman, and T. F. Liao, The SAGE Encyclopedia of Social Science Research Methods. Newbury Park, CA, USA: Sage, 2003.
  3. C.-L. Hwang and K. Yoon, Multiple Attribute Decision Making: Methods and Applications: A State-of-the-Art Survey, vol. 24. New York, NY, USA: Springer-Verlag, 1981, p. 113.
  4. J. Cito, J. Wettinger, L. E. Lwakatare, M. Borg, and F. Li, ''Feedback from operations to software development-A DevOps perspective on runtime metrics and logs,'' in Proc. Int. Workshop Softw. Eng. Aspects Continuous Develop. New Paradigms Softw. Prod. Deployment. Cham, Switzerland: Springer, 2018, pp. 184-195.
  5. A. F. Nogueira, J. C. B. Ribeiro, M. A. Zenha-Rela, and A. Craske, ''Improving la Redoute's CI/CD pipeline and DevOps processes by applying machine learning techniques,'' in Proc. 11th Int. Conf. Qual. Inf. Commun. Technol. (QUATIC), Sep. 2018, pp. 282-286.
  6. MUHAMMAD AZEEM AKBAR received the M.Sc. and M.S. degrees in computer science from the University of Agriculture Faisalabad (UAF), Faisalabad, Pakistan, and the Ph.D. degree in software engineering from Chongqing University, China. He is currently working as a Postdoc- toral researcher with the Nanjing University of Aeronautics and Astronautics, Nanjing, China. He has published more than 30 research articles in well-reputed journals and conferences. He has an outstanding academic carrier. His research interests are global software development, requirements engineering, empirical studies, global software requirements change management, DevOps implementation, software defect prediction, the Internet of Things, code recommender systems, and software risk management. AHMED ALSANAD received the Ph.D. degree in computer science from the De Montfort Univer- sity, U.K., in 2013. He is currently an Associate Professor with the Information System Depart- ment and the Chair Member of pervasive and mobile computing with the College of Computer and Information Sciences (CCIS), King Saud Uni- versity, Riyadh, Saudi Arabia. He has authored or coauthored more than 12 publications, includ- ing refereed IEEE/ACM/Springer journals, con- ference papers, and book chapters. His research interests include cloud computing, health informatics, ERP, and CRM. ABDU GUMAEI received the B.S. degree in com- puter science from the Computer Science Depart- ment, Al-Mustansiriya University, Baghdad, Iraq, the master's degree in computer science from the Computer Science Department, King Saud Univer- sity, Riyadh, Saudi Arabia, and the Ph.D. degree in computer science from King Saud University. He has worked as a Lecturer and taught many courses, such as programming languages at the Department of Information Systems, King Saud University. He is currently an Assistant Professor of computer science. His main areas of interest are software engineering, image processing, computer vision, and machine learning. He has several researches in the field of image processing. He has received a patent from the United States Patent and Trademark Office (USPTO), in 2013.

Cited by

Sorted by citations

Related

Authors
Rafi, S., Yu, W., Akbar, M. A., Alsanad, A., & Gumaei, A.
Affiliations
  • Chongqing University of Posts and Telecommunications (CN)
  • Nanjing University of Aeronautics and Astronautics (CN)
  • King Saud University (SA)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020
Volume
8
Pages
46958-46980
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2020.2976803
OpenAlex
W3007592916
Cited by
37
References
61
Citations by year
2026: 4 · 2025: 4 · 2024: 4 · 2023: 7 · 2022: 8 · 2021: 5 · 2020: 4 · 2018: 1
FWCI
3.82
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Natural Science Foundation of China (17XFX013)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
Keywords
DevOps, data quality assessment, heterogeneous data integration, key factors, fuzzy TOPSIS, multi-criteria decision making, real-time analytics, data visualization, missing data

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review