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   Lu, Xin; Liu, Tianle; Liu, Hanzhong; Ding, Peng (August 2022, Biometrika)

   Summary Complete randomization balances covariates on average, but covariate imbalance often exists in finite samples. Rerandomization can ensure covariate balance in the realized experiment by discarding the undesired treatment assignments. Many field experiments in public health and social sciences assign the treatment at the cluster level due to logistical constraints or policy considerations. Moreover, they are frequently combined with re-randomization in the design stage. We define cluster rerandomization as a cluster-randomized experiment compounded with rerandomization to balance covariates at the individual or cluster level. Existing asymptotic theory can only deal with rerandomization with treatments assigned at the individual level, leaving that for cluster rerandomization an open problem. To fill the gap, we provide a design-based theory for cluster rerandomization. Moreover, we compare two cluster rerandomization schemes that use prior information on the importance of the covariates: one based on the weighted Euclidean distance and the other based on the Mahalanobis distance with tiers of covariates. We demonstrate that the former dominates the latter with optimal weights and orthogonalized covariates. Last but not least, we discuss the role of covariate adjustment in the analysis stage, and recommend covariate-adjusted procedures that can be conveniently implemented by least squares with the associated robust standard errors. 

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   Cai, Yuanfang; Kazman, Rick (December 2023, Information and Software Technology)

   In this paper we reflect on our decade-long journey of creating, evolving, and evaluating a number of software design concepts and technical debt management technologies. These include: a novel maintainability metric, a new model for representing design information, a suite of design anti-patterns, and a formalized model of design debt. All of these concepts are rooted in options theory, and they all share the objective of helping a software project team quantify and visualize major design principles, and address the very real maintainability challenges faced by their organizations in practice. The evolution of our research has been propelled by our continuous interactions with industrial collaborators. For each concept, technology, and supporting tool, we embarked on an ambitious program of empirical validation—in “the lab”, with industry partners, and with open source projects. We reflect on the successes of this research and on areas where significant challenges remain. In particular, we observe that improved software design education, both for students and professional developers, is the prerequisite for our research and technology to be widely adopted. During this journey, we also observed a number of gaps: between what we offer in research and what practitioners need, between management and development, and between debt detection and debt reduction. Addressing these challenges motivates our research moving forward. 

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3. Decentering Through Design: Bridging Posthuman Theory with More-than-Human Design Practices

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4. On the Use of Utility Theory in Engineering Design

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   Abbas, Ali E.; Cadenbach, Andrea H. (June 2018, IEEE Systems Journal)

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   Miao, Wei; Narayanan, Vignesh; Li, Jr-Shin (January 2022, IEEE Transactions on Neural Networks and Learning Systems)