TY - JOUR
T1 - Comparing Methods for Large-Scale Agile Software Development
T2 - A Systematic Literature Review
AU - Edison, Henry
AU - Wang, Xiaofeng
AU - Conboy, Kieran
N1 - Publisher Copyright:
© 1976-2012 IEEE.
PY - 2022/8/1
Y1 - 2022/8/1
N2 - Following the highly pervasive and effective use of agile methods at the team level, many software organisations now wish to replicate this success at the organisational level, adopting large-scale agile methods such as SAFe, Scrum-at-Scale, and others. However, this has proven significantly challenging. An analysis of the extant literature reveals a disparate set of studies across each individual method, with no cross-method comparison based on empirical evidence. This systematic literature review compares the main large-scale agile methods, namely SAFe, LeSS, Scrum-at-Scale, DAD, and the Spotify model. It is the first study to analyse and compare each of the method's principles, practices, tools, and metrics in a standardised manner. For each method, it presents not just the original method specifications but also all extensions and modifications to each method proposed by subsequent empirical research. It includes in this comparison not just commercial large-scale methods but also those that have been custom-built in organisations such as Nokia, Ericsson, and others. Based on the findings reported in this study, practitioners can make a more informed decision as to which commercial method or method component or, indeed, custom-built method is better suited to their needs. Our study reveals a number of theoretical and practical issues in the current literature, such as an emphasis on the practices of commercial frameworks at the expense of their underlying principles, or indeed any of the custom method. A set of challenges and success factors associated with the use of large-scale agile methods are identified. The study also identifies a number of research gaps to be addressed across methods.
AB - Following the highly pervasive and effective use of agile methods at the team level, many software organisations now wish to replicate this success at the organisational level, adopting large-scale agile methods such as SAFe, Scrum-at-Scale, and others. However, this has proven significantly challenging. An analysis of the extant literature reveals a disparate set of studies across each individual method, with no cross-method comparison based on empirical evidence. This systematic literature review compares the main large-scale agile methods, namely SAFe, LeSS, Scrum-at-Scale, DAD, and the Spotify model. It is the first study to analyse and compare each of the method's principles, practices, tools, and metrics in a standardised manner. For each method, it presents not just the original method specifications but also all extensions and modifications to each method proposed by subsequent empirical research. It includes in this comparison not just commercial large-scale methods but also those that have been custom-built in organisations such as Nokia, Ericsson, and others. Based on the findings reported in this study, practitioners can make a more informed decision as to which commercial method or method component or, indeed, custom-built method is better suited to their needs. Our study reveals a number of theoretical and practical issues in the current literature, such as an emphasis on the practices of commercial frameworks at the expense of their underlying principles, or indeed any of the custom method. A set of challenges and success factors associated with the use of large-scale agile methods are identified. The study also identifies a number of research gaps to be addressed across methods.
KW - Large-scale agile
KW - challenges and success factors
KW - critical assessment
KW - systematic literature review
UR - http://www.scopus.com/inward/record.url?scp=85103243626&partnerID=8YFLogxK
U2 - 10.1109/TSE.2021.3069039
DO - 10.1109/TSE.2021.3069039
M3 - Article
SN - 0098-5589
VL - 48
SP - 2709
EP - 2731
JO - IEEE Transactions on Software Engineering
JF - IEEE Transactions on Software Engineering
IS - 8
ER -