64 research outputs found

    Designing Declarative Language Tutorials: A Guided and Individualized Approach

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    A Pilot Study of the Safety and Usability of the Obsidian Blockchain Programming Language

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    Gradual Program Analysis for Null Pointers

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    Static analysis tools typically address the problem of excessive false positives by requiring programmers to explicitly annotate their code. However, when faced with incomplete annotations, many analysis tools are either too conservative, yielding false positives, or too optimistic, resulting in unsound analysis results. In order to flexibly and soundly deal with partially-annotated programs, we propose to build upon and adapt the gradual typing approach to abstract-interpretation-based program analyses. Specifically, we focus on null-pointer analysis and demonstrate that a gradual null-pointer analysis hits a sweet spot, by gracefully applying static analysis where possible and relying on dynamic checks where necessary for soundness. In addition to formalizing a gradual null-pointer analysis for a core imperative language, we build a prototype using the Infer static analysis framework, and present preliminary evidence that the gradual null-pointer analysis reduces false positives compared to two existing null-pointer checkers for Infer. Further, we discuss ways in which the gradualization approach used to derive the gradual analysis from its static counterpart can be extended to support more domains. This work thus provides a basis for future analysis tools that can smoothly navigate the tradeoff between human effort and run-time overhead to reduce the number of reported false positives

    Structuring Documentation to Support State Search: A Laboratory Experiment about Protocol Programming

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    Abstract. Application Programming Interfaces (APIs) often define object protocols. Objects with protocols have a finite number of states and in each state a different set of method calls is valid. Many researchers have developed protocol verification tools because protocols are notoriously difficult to follow correctly. However, recent research suggests that a major challenge for API protocol programmers is effectively searching the state space. Verification is an ineffective guide for this kind of search. In this paper we instead propose Plaiddoc, which is like Javadoc except it organizes methods by state instead of by class and it includes explicit state transitions, state-based type specifications, and rich state relationships. We compare Plaiddoc to a Javadoc control in a betweensubjects laboratory experiment. We find that Plaiddoc participants complete state search tasks in significantly less time and with significantly fewer errors than Javadoc participants

    Toward Semantic Foundations for Program Editors

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    Programming language definitions assign formal meaning to complete programs. Programmers, however, spend a substantial amount of time interacting with incomplete programs - programs with holes, type inconsistencies and binding inconsistencies - using tools like program editors and live programming environments (which interleave editing and evaluation). Semanticists have done comparatively little to formally characterize (1) the static and dynamic semantics of incomplete programs; (2) the actions available to programmers as they edit and inspect incomplete programs; and (3) the behavior of editor services that suggest likely edit actions to the programmer based on semantic information extracted from the incomplete program being edited, and from programs that the system has encountered in the past. This paper serves as a vision statement for a research program that seeks to develop these "missing" semantic foundations. Our hope is that these contributions, which will take the form of a series of simple formal calculi equipped with a tractable metatheory, will guide the design of a variety of current and future interactive programming tools, much as various lambda calculi have guided modern language designs. Our own research will apply these principles in the design of Hazel, an experimental live lab notebook programming environment designed for data science tasks. We plan to co-design the Hazel language with the editor so that we can explore concepts such as edit-time semantic conflict resolution mechanisms and mechanisms that allow library providers to install library-specific editor services
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