Ross Jeffery

Qinghua Lu, An Binh Tran, Ingo Weber et al.

Blockchain has attracted broad interests to build decentralised applications. Blockchain has attracted broad interests to build decentralised applications. However, developing such applications without introducing vulnerabilities is hard for developers, not the least because the deployed code is immutable and can be called by anyone with access to the network. Model-driven engineering (MDE) helps to reduce those risks, by combining proven code snippets as per the model specification, which is easier to understand than source code. Therefore, in this paper, we present an approach for integrated MDE across business processes and asset management (e.g. for settlement). Our approach includes methods for fungible/non-fungible asset registration, escrow for conditional payment, and asset swap. The proposed MDE approach is implemented in a smart contract generation tool called Lorikeet, and evaluated in terms of feasibility, functional correctness, and cost effectiveness.

Sung Une Lee, Liming Zhu, Ross Jeffery

As platform ecosystems are growing by platform users' data, the importance of data governance has been highlighted. In particular, how to share control and decision rights with platform users are regarded as significant design issues since the role of them is increasing. Platform context should be considered when designing data governance in platform ecosystems (i.e. centralized/decentralized governance). However, there is limited research on this issue. Existing models focus on characteristics for enterprises. This results in limited support for platform ecosystems where there are different types of business context such as open strategies or platform maturity. This paper develops a contingency model for platform ecosystems including distinctive contingency factors. The study then discusses which data governance factors should be carefully considered and strengthened for each contingency in order to succeed in governance and to win market. A case study is performed to validate our model and to show its practical implications.

Adam Trendowicz, Jürgen Münch, Ross Jeffery

Effort estimation is a key factor for software project success, defined as delivering software of agreed quality and functionality within schedule and budget. Traditionally, effort estimation has been used for planning and tracking project resources. Effort estimation methods founded on those goals typically focus on providing exact estimates and usually do not support objectives that have recently become important within the software industry, such as systematic and reliable analysis of causal effort dependencies. This article presents the results of a study of software effort estimation from an industrial perspective. The study surveys industrial objectives, the abilities of software organizations to apply certain estimation methods, and actually applied practices of software effort estimation. Finally, requirements for effort estimation methods identified in the survey are compared against existing estimation methods.