Proceedings of the 33rd Annual Workshop of the Psychology of Programming Interest Group

This is the Proceedings of the 33rd Annual Workshop of the Psychology of Programming Interest Group (PPIG). This was the first PPIG to be held physically since 2019, following the two online-only PPIGs in 2020 and 2021, both during the Covid pandemic. It was also the first PPIG conference to be designed specifically for hybrid attendance. Reflecting the theme, it was hosted by Music Computing Lab at the Open University in Milton Keynes

Highlighting model elements to improve OCL comprehension

Models, metamodels, and model transformations play a central role in Model-Driven Development (MDD). Object Constraint Language (OCL) was initially proposed as part of the Unified Modeling Language (UML) standard to add the precision and validation capabilities lacking in its diagrams, and to express well-formedness rules in its metamodel. OCL has several other applications, such as defining design metrics, code-generation templates, or validation rules for model transformations, required in MDD. Learning OCL as part of a UML course at the university would seem natural but is still the exception rather than the rule. We believe that this is mainly due to a widespread perception that OCL is hard to learn, as gleaned from claims made in the literature. Based on data gathered over the past school years from numerous undergraduate students of di↵erent Software Engineering courses, we analyzed how learning design by contract clauses with UML+OCL compares with several other Software Engineering Body Of Knowledge (SWEBOK) topics. The outcome of the learning process was collected in a rigorous setup, supported by an e-learning platform. We performed inferential statistics on that data to support our conclusions and identify the relevant explanatory variables for students’ success/failure. The obtained findings lead us to extend an existing OCL tool with two novel features: one is aimed at OCL apprentices and goes straight to the heart of the matter by allowing to visualize how OCL expressions traverse UML class diagrams; the other is intended for researchers and allows to compute OCL complexity metrics, making it possible to replicate a research study like the one we are presenting.Modelos, metamodelos e transformações de modelo desempenham um papel central em MDD. OCL foi inicialmente proposta como parte da UML para adicionar os recursos de precisão e validação que faltavam nestes diagramas, e também para expressar regras de boa formação no metamodelo. OCL possui outras aplicações, tais como definir métricas de desenho, modelos de geração de código ou regras de validação para transformações de modelo, exigidas em MDD. Aprender OCL como parte de um curso de UML na universidade parecia portanto natural, não sendo no entanto o que se verifica. Acreditamos que isso se deva a uma percepção generalizada de que OCL é difícil de aprender, tendo em conta afirmações feitas na literatura. Com base em dados recolhidos em anos letivos anteriores de vários alunos de licenciatura de diferentes cursos de Engenharia de Software, analisámos como a aprendizagem por cláusulas contratuais de UML + OCL se compara a outros tópicos do SWEBOK. O resultado do processo de aprendizagem foi recolhido de forma rigorosa, apoiado por uma plataforma de e-learning. Realizámos estatísticas inferenciais sobre os dados para apoiar as nossas conclusões, de forma a identificar as variáveis explicativas relevantes para o sucesso / fracasso dos alunos. As conclusões obtidas levaram-nos a estender uma ferramenta OCL com duas novas funcionalidades: a primeira é voltada para os estudantes de OCL e permite visualizar como as expressões percorrem um diagrama de classes UML; a segunda é voltada para investigadores e permite calcular métricas de complexidade OCL, habilitando a réplica de um estudo semelhante ao apresentado