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We present an enumerative program synthesis framework calledcomponent-based refactoringthat can refactor “direct” style code that does not use library components into equivalent “combinator” style code that does use library components. This framework introduces a sound but incomplete technique to check the equivalence of direct code and combinator code calledequivalence by canonicalizationthat does not rely on input-output examples or logical specifications. Moreover, our approach can repurpose existing compiler optimizations, leveraging decades of research from the programming languages community. We instantiated our new synthesis framework in two contexts: (i) higher-order functional combinators such asmapandfilterin the staticallytyped functional programming language Elm and (ii) high-performance numerical computing combinators provided by the NumPy library for Python. We implemented both instantiations in a tool calledCobblerand evaluated it on thousands of real programs to test the performance of the component-based refactoring framework in terms of execution time and output quality. Our work offers evidence that synthesis-backed refactoring can apply across a range of domains without specification beyond the input program. 

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. 

In the United States, public defenders (lawyers assigned to people accused of crimes who cannot afford a private attorney) serve as an essential bulwark against wrongful arrest and incarceration for low-income and marginalized people. Public defenders have long been overworked and under-resourced. However, these issues have been compounded by increases in the volume and complexity of data in modern criminal cases. We explore the technology needs of public defenders through a series of semi-structured interviews with public defenders and those who work with them. We find that public defenders' ability to reason about novel surveillance data is woefully inadequate not only due to a lack of resources and knowledge, but also due to the structure of the criminal justice system, which gives prosecutors and police (in partnership with private companies) more control over the type of information used in criminal cases than defense attorneys. We find that public defenders may be able to create fairer situations for their clients with better tools for data interpretation and access. Therefore, we call on technologists to attend to the needs of public defenders and the people they represent when designing systems that collect data about people. Our findings illuminate constraints that technologists and privacy advocates should consider as they pursue solutions. In particular, our work complicates notions of individual privacy as the only value in protecting users' rights, and demonstrates the importance of data interpretation alongside data visibility. As data sources become more complex, control over the data cannot be separated from access to the experts and technology to make sense of that data. The growing surveillance data ecosystem may systematically oppress not only those who are most closely observed, but groups of people whose communities and advocates have been deprived of the storytelling power over their information.