Challenges and Opportunities in Automating ...
Document type :
Communication dans un congrès avec actes
DOI :
Title :
Challenges and Opportunities in Automating DBMS: A Qualitative Study
Author(s) :
Wang, Yifan [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Université de Lille
Orange Innovation
Bourhis, Pierre [Auteur]
Université de Lille
Centre National de la Recherche Scientifique [CNRS]
Self-adaptation for distributed services and large software systems [SPIRALS]
Rouvoy, Romain [Auteur]
Université de Lille
Self-adaptation for distributed services and large software systems [SPIRALS]
Royer, Patrick [Auteur]
Orange Innovation
Self-adaptation for distributed services and large software systems [SPIRALS]
Université de Lille
Orange Innovation
Bourhis, Pierre [Auteur]

Université de Lille
Centre National de la Recherche Scientifique [CNRS]
Self-adaptation for distributed services and large software systems [SPIRALS]
Rouvoy, Romain [Auteur]

Université de Lille
Self-adaptation for distributed services and large software systems [SPIRALS]
Royer, Patrick [Auteur]
Orange Innovation
Conference title :
ASE '24: 39th IEEE/ACM International Conference on Automated Software Engineering
City :
Sacramento - Californie
Country :
Etats-Unis d'Amérique
Start date of the conference :
2024-10-28
Journal title :
ASE2024 Industry Track
Publisher :
ACM
Publication date :
2024-10-24
English keyword(s) :
reference → Empirical studies
Social and professional topics → Automation Automation DBMS Empirical research Qualitative methods
Social and professional topics → Automation Automation
DBMS
Empirical research
Qualitative methods
Social and professional topics → Automation Automation DBMS Empirical research Qualitative methods
Social and professional topics → Automation Automation
DBMS
Empirical research
Qualitative methods
HAL domain(s) :
Informatique [cs]
English abstract : [en]
Background. In recent years, the volume and complexity of data handled by Database Management Systems (DBMS) have surged, necessitating greater efforts and resources for efficient administration. In response, numerous ...
Show more >Background. In recent years, the volume and complexity of data handled by Database Management Systems (DBMS) have surged, necessitating greater efforts and resources for efficient administration. In response, numerous automation tools for DBMS administration have emerged, particularly with the progression of AI and machine learning technologies. However, despite these advancements, the industry-wide adoption of such tools remains limited.Aims. This qualitative research aims to delve into the practices of DBMS users, identifying their difficulties around DBMS administration. By doing so, we intend to uncover key challenges and prospects for DBMS administration automation, thereby promoting its development and adoption.Method. This paper presents the findings of a qualitative study we conducted in an industrial setting to explore this particular issue. The study involved conducting in-depth interviews with 11 DBMS experts, and we analyzed the data to derive a set of implications.Results. We argue that our study offers two important contributions: firstly, it provides valuable insights into the challenges and opportunities of DBMS administration automation through interviewees' perceptions, routines, and experiences. Secondly, it presents a set of findings that can be derived to useful implications and promote DBMS administration automation.<p>Conclusions. This paper presents an empirical study conducted in an industrial context that examines the challenges and opportunities of DBMS administration automation within a particular company. Although the study's findings may not apply to all companies, we believe the results provide a valuable body of knowledge with implications that can be useful for future research endeavors.</p>Show less >
Show more >Background. In recent years, the volume and complexity of data handled by Database Management Systems (DBMS) have surged, necessitating greater efforts and resources for efficient administration. In response, numerous automation tools for DBMS administration have emerged, particularly with the progression of AI and machine learning technologies. However, despite these advancements, the industry-wide adoption of such tools remains limited.Aims. This qualitative research aims to delve into the practices of DBMS users, identifying their difficulties around DBMS administration. By doing so, we intend to uncover key challenges and prospects for DBMS administration automation, thereby promoting its development and adoption.Method. This paper presents the findings of a qualitative study we conducted in an industrial setting to explore this particular issue. The study involved conducting in-depth interviews with 11 DBMS experts, and we analyzed the data to derive a set of implications.Results. We argue that our study offers two important contributions: firstly, it provides valuable insights into the challenges and opportunities of DBMS administration automation through interviewees' perceptions, routines, and experiences. Secondly, it presents a set of findings that can be derived to useful implications and promote DBMS administration automation.<p>Conclusions. This paper presents an empirical study conducted in an industrial context that examines the challenges and opportunities of DBMS administration automation within a particular company. Although the study's findings may not apply to all companies, we believe the results provide a valuable body of knowledge with implications that can be useful for future research endeavors.</p>Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :
Files
- document
- Open access
- Access the document
- QR_ACM%20%281%29.pdf
- Open access
- Access the document