TY - JOUR
T1 - Multicriteria based decision making of DevOps data quality assessment challenges using fuzzy TOPSIS
AU - Rafi, Saima
AU - Yu, Wu
AU - Akbar, Muhammad Azeem
AU - Alsanad, Ahmed
AU - Gumaei, Abdu
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - DevOps data quality assessment
KW - empirical investigation
KW - fuzzy TOPSIS
UR - http://www.scopus.com/inward/record.url?scp=85082004583&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.2976803
DO - 10.1109/ACCESS.2020.2976803
M3 - Article
AN - SCOPUS:85082004583
SN - 2169-3536
VL - 8
SP - 46958
EP - 46980
JO - IEEE Access
JF - IEEE Access
M1 - 9015992
ER -