Big Data analytics in Agile software development: A systematic mapping study

2020 ◽  
pp. 106448
Author(s):  
Katarzyna Biesialska ◽  
Xavier Franch ◽  
Victor Muntés-Mulero
Keyword(s):  
Big Data ◽  
Software Development ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Agile Software Development ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Agile Software

Effort Estimation in Agile Software Development: A Systematic Mapping Study

2021 ◽  
Author(s):  
Nour elhouda Farih ◽  
Khalid Nafil ◽  
Rochdi El Messousi
Keyword(s):  
Software Development ◽  
Agile Software Development ◽  
Development Project ◽  
Agile Development ◽  
Systematic Mapping Study ◽  
Effort Estimation ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Mode Of Development ◽  
Agile Software

Context: Estimating effort has always been considered an important element at the start of each software development project. The challenge of estimating the effort of software development lies in its precision. With the emergence of agile methodologies, methods for effort estimation (EE) had to adapt to this new development path. In this article, we are conducting a systematic mapping study on effort estimation in the context of agile software development. Objective: we want to identify the estimation approaches and techniques used in the context of agile development to better understand the specifics and trends relating to this mode of development. Method: we conducted a systematic mapping study by adopting the guideline explained in[1] [2]. A systematic review of the literature [3] has already been carried out for publications between 2001 and 2013. This work is an extension of this previous study. We queried 5 electronic databases. Conclusion: We retrieved 11350 paper from five electronic databases. A total of 108 papers is selected after applying the inclusion and exclusion criteria. Based on the results, there is a general increase over the years of studies concerning effort estimation in agile software development.

scholarly journals Applying Big Data Analytics in Higher Education

2021 ◽  
Vol 17 (3) ◽  
pp. 29-51
Author(s):  
Adel Alkhalil ◽  
Magdy Abd Elrahman Abdallah ◽  
Azizah Alogali ◽  
Abdulaziz Aljaloud
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Research Direction ◽  
Future Research ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Ongoing Research ◽  
Motivation Factors

Higher education systems (HES) have become increasingly absorbed in applying big data analytics due to competition as well as economic pressures. Many studies have been conducted that applied big data analytics in HES; however, a systematic review (SR) of the research is scarce. In this paper, the authors conducted a systematic mapping study to address this deficiency. The qualitative and quantitative analysis of the mapping study resulted in highlighting the research progression over the last decade, and identification of three major themes, 12 subthemes, 10 motivation factors, 10 major challenges, three categories of tools and support techniques, and 16 models for applying big data analytics in higher education. This result contributes to the ongoing research on applying big data analytics in HES. It provides a better understanding of the level of contribution to research as well as identifies gaps for future research direction.

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