A Requirements-Driven Methodology for the Proper Selection and Configuration of Blockchains

Staderini, Mirko; Schiavone, Enrico; Bondavalli, Andrea

doi:10.1109/srds.2018.00031

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…The Boolean Decision Tree (BDT) is an MCDM method to choose one of the available and feasible decision alternatives. Staderini et al [19] propose a BDT-based requirementsdriven methodology to support decision-makers to select suitable blockchain platform category (i.e., public or private, and permissionless or permissioned). Moreover, their proposed method assists the decision-makers with the configuration of selected blockchain platforms.…”

Section: Related Workmentioning

confidence: 99%

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Decision Support for Blockchain Platform Selection: Three Industry Case Studies

Farshidi

Jansen

España

et al. 2020

IEEE Trans. Eng. Manage.

139

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Blockchain technology has received significant attention recently, as it offers a reliable decentralized infrastructure for all kinds of business transactions. Software-producing organizations are increasingly considering blockchain technology for inclusion into their software products. Selecting the best fitting blockchain platform requires the assessment of its functionality, adaptability, and compatibility to the existing software product. Novice software developers and architects are not experts in every domain, so they should either consult external experts or acquire knowledge themselves. The decision-making process gets more complicated as the number of decision-makers, alternatives, and criteria increases. Hence, a decision model is required to externalize and organize knowledge regarding the blockchain platform selection context. Recently, we designed a decision support system to use such decision models to support decision-makers with their technology selection problems in software production. In this article, we introduce a decision model for the blockchain platform selection problem. The decision model has been evaluated through three real-world case studies at three software-producing organizations. The case-study participants asserted that the approach provides significantly more insight into the blockchain platform selection process, provides a richer prioritized option list than if they had done their research independently, and reduces the time and cost of the decision-making process.

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Section: Related Workmentioning

confidence: 99%

“…The number of criteria of BDT-based approaches [19]- [22] is limited, i.e., under ten, as processing the large decision-trees is time-consuming and complicated. BDT-based approaches suggest only one solution at the end of each evaluation.…”

Section: Table I Comparision Of Selected Studies With the Literature mentioning

confidence: 99%

Decision Support for Blockchain Platform Selection: Three Industry Case Studies

Farshidi

Jansen

España

et al. 2020

IEEE Trans. Eng. Manage.

139

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“…In literature, a wide range of MCDM techniques has been introduced to address MCDM problems in software production, such as the analytic hierarchy process (AHP) (Garg et al, 2011;Jusoh et al, 2014), Technique for Order Preference by Similarity to Ideal Solution(TOPSIS) (Oztaysi, 2014;Tang et al, 2019), analytic network process (ANP) (Lee & Kim, 2000;Yazgan et al, 2009), case-based reasoning (CBR) (Jadhav & Sonar, 2011;Li et al, 2009), fuzzy set theory (Rodriguez et al, 2017;Rouhani & Ravasan, 2015), Boolean Decision Tree (BDT) (Pahl et al, 2018;Staderini et al, 2018), weighted scoring method (WSM) (Davies & Reeves, 2010;Delgado et al, 2015), genetic algorithm (GA) (Doval et al, 1999;Guo et al, 2011), mathematical programming (Karsak & Özogul, 2009;Sahay & Gupta, 2003), and their hybrids.…”

Section: Multi-criteria Decision-making Techniquesmentioning

confidence: 99%

“…The Boolean Decision Tree (BDT) is an MCDM method to chose one of the available and feasible decision alternatives. Staderini et al (2018) propose a BDTbased requirements-driven methodology to support decision-makers to select suitable blockchain platform category (i.e., public or private, and permissionless or permissioned). Moreover, their proposed method assists the decision-makers with the configuration of selected blockchain platforms.…”

Section: Related Workmentioning

confidence: 99%

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Multi-Criteria Decision-Making in Software Production

Farshidi¹

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Undertaking this Ph.D. has been a truly life-changing experience for me, and it would not have been possible to do without the support and guidance that I received from many people. The entire list of people who encouraged me during my Ph.D. is prolonged, whether within the research itself, forming a suitable environment, or even giving personal support. Probably, I will have forgotten to recall all names.First and foremost, I would like to thank Slinger Jansen. To me, Slinger was more than a supervisor. He was an inspiring and enthusiastic co-promoter who showed me the right course of action in all situations. His encouragements and optimism were indispensable to keep me motivated. Slinger taught me how to do research and then report findings correctly to scientific communities.I would like to express my sincere gratitude to Sjaak Brinkkemper. He actively supported my research and encouraged me to write down high-quality scientific papers. With a few questions, he always helped me to focus on contributions and structure my findings. Most of his remarks will be with me for my entire career as a researcher.I would like to say a special thank you to Rolf de Jong. His support, guidance, and comprehensive insights into the software engineering field have made an inspiring experience for me. And special thanks to Michiel Overeem, whose support as an experienced software engineer and an eager researcher allowed my studies to go the extra mile (sorry for all the additional work, Michiel!).Doing research and writing papers in collaboration with others was a pleasant experience as it allowed me to gain more insight and knowledge regarding what I was working on. I am profoundly grateful for my co-authors' hard work, who have not been mentioned above, and their substantial contribution to uplift the studies presented in this dissertation. Thanks, Sergio España, Jan Martijn van der Werf, Rolf de Jong, and Jacco Verkleij. I am also thankful to my motivated students who assisted me in the research projects, especially Elena Baninemeh, Andrey Krupskiy, Baharak Bakhtiari, and Mahdi Deldar. I am delighted to have worked with you, and I look forward to working with you again.My research would have been impossible without the feedback from domain experts and case study participants from various software producing organizations that participated in the research projects' evaluation phases in this dissertation.The work presented in this dissertation has been critically assessed and approved by an outstanding reading committee to whom I am more than grateful: Patricia Lago, Krzysztof Wnuk, Gabriele Keller, Bedir Tekinerdogan, and Diomidis Spinellis. I also vi Acknowledgments want to thank the numerous anonymous reviewers of the journals and conferences. Often I received constructive feedback that helped me to improve the quality of my publications.To conclude, I cannot forget to thank my parents, beloved sister, and friends for all the unconditional love and support during my Ph.D. journey.

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