ABSTRACT
Automated scoring systems are software applications that rely heavily on machine learning (ML) and natural language processing (NLP). Existing literature on automated scoring focuses on functionality and evaluation metrics. This chapter discusses the role of software robustness as another important dimension of automated scoring. Our intended audience is measurement scientists and psychometricians who are generally not exposed to the technical aspects of software development. This chapter describes the motivation for writing robust software for automated scoring and provides a brief introduction to the processes by which such software may be developed and deployed.
