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Programming tools are increasingly integral to research and analysis in myriad domains, including specialized areas with no formal relation to computer science. Embedded domain-specific languages (eDSLs) have the potential to serve these programmers while placing relatively light implementation burdens on language designers. However, barriers to eDSL use reduce their practical value and adoption. In this paper, we aim to deepen our understanding of how programmers use eDSLs and identify user needs to inform future eDSL designs. We performed a contextual inquiry (9 participants) with domain experts using Mimi, an eDSL for climate change economics modeling. A thematic analysis identified five key themes, including: the interaction between the eDSL and the host language has significant and sometimes unexpected impacts on eDSL user experience, and users preferentially engage with domain-specific communities and code templates rather than host language resources. The needs uncovered in our study offer design considerations for future eDSLs and suggest directions for future DSL usability research. 

Creating modern software inevitably requires using application programming interfaces (APIs). While software developers can sometimes use APIs by simply copying and pasting code examples, a lack of robust knowledge of how an API works can lead to defects, complicate software maintenance, and limit what someone can express with an API. Prior work has uncovered the many ways that API documentation fails to be helpful, though rarely describes precisely why. We present a theory of robust API knowledge that attempts to explain why, arguing that effective understanding and use of APIs depends on three components of knowledge: (1) the domain concepts the API models along with terminology, (2) the usage patterns of APIs along with rationale, and (3) facts about an API’s execution to support reasoning about its runtime behavior. We derive five hypotheses from this theory and present a study to test them. Our study investigated the effect of having access to these components of knowledge, finding that while learners requested these three components of knowledge when they were not available, whether the knowledge helped the learner use or understand the API depended on the tasks and likely the relevance and quality of the specific information provided. The theory and our evidence in support of its claims have implications for what content API documentation, tutorials, and instruction should contain and the importance of giving the right information at the right time, as well as what information API tools should compute, and even how APIs should be designed. Future work is necessary to both further test and refine the theory, as well as exploit its ideas for better instructional design.