Spreadsheets are heavily used in industry as they are easy to create and evolve. Initially, they are often small and simple, but, over time, they can become very complex. In many ways, spreadsheets are similar to “professional” software: both concern the storage and manipulation of data, and the presentation of results to the user. But unlike in “professional” software, activities like design, implementation, and maintenance in spreadsheets have to be undertaken by end users, not trained professional developers. This makes applying methods and techniques from other software domains a challenging task.

The role of International Workshop on Software Engineering Methods in Spreadsheets (SEMS) is to provide an annual event where researchers can meet and exchange their ideas. SEMS serves as a platform for early feedback for new techniques and tools. The SEMS program will include a keynote, the presentation of short and long research papers, tool demonstrations, and a discussion session. The intended audience is a mixture of spreadsheet researchers and professionals.


An important arena for building human-adaptive socio-technical systems is new designs that address the solving of global challenges such as climate change, drug-resistant diseases, and nuclear proliferation. Such “wicked” problems necessitate the engagement and collaboration of multidisciplinary teams of people with the support of appropriate computer-based tools.

This workshop will address the following questions related to the design of such systems: What possible new forms of human problem-solving experiences should be supported by new online tools? What social structures and workflows may need to be supported? How should students and professionals be educated and trained in order to be able to function most productively in these new human-technical environments for problem solving? What are potential roles for crowdsourcing, artificial intelligence, machine learning, intelligent tutoring, gamification, and the Internet of Things in these systems? How should systems enable humans to compensate for biases in Machine Learning processes? What might existing and possible future theories of problem solving contribute to the design of these systems? What provisions should be made to engage diverse solver communities in terms of stakeholders, genders, socio-economic status, and culture? What should a research agenda in this field consist of?