Alloy4Fun Dataset for 2022/23

This dataset contains models submitted by students in the Alloy4Fun platform to solve the challenge models from various editions of formal methods courses in the University of Minho (UM) and the University of Porto (UP) between the fall of 2019 and the spring of 2023, totalling about 100.000 entries. Participants include those enrolled in the optional MSc course "Specification and Modelling" (EM) and the mandatory MSc course "Formal Methods in Software Engineering" (MFES) in UM, and the optional MSc course "Formal Methods for Critical Systems" (MFS) in UP. Note that since the challenges' permalinks are publicly available, the dataset may contain submissions from other participants outside the classroom context.

The analysis of the 2021 dataset is reported in the Science of Computer Programming paper "Experiences on Teaching Alloy with an Automated Assessment Platform" (extending the ABZ'20 conference version analysing the 2020 dataset).

Each entry of the dataset registers either an execution (which may have returned a result or an error) or the creation of a permalink for sharing, and contains:

• _id: the id of the interaction
• time: the timestamp of its creation
• derivationOf: the parent entry
• original: the first ancestor with secrets (always the same within an exercise)
• code: the complete code of the model (excluding the secrets defined in the original entry) (with student comments removed)
• sat: whether the command was satisfiable (counter-example found for checks), or -1 when error thrown [only for executions]
• cmd_i: the index of the executed command [only for executions]
• cmd_n: the name of the executed command [only for successful executions, i.e. no error thrown]
• cmd_c: whether the command was a check [only for successful executions, i.e. no error thrown]
• msg: the error or warning message [only for successful executions with warnings or when error thrown]
• theme: the visualisation theme [only for sharing entries]

User comments were removed from the code to guarantee anonymization.