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Register a new userThe Usability of Ownership
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Added by
Will Crichton
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Authors
Will Crichton
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Ownership is the concept of tracking aliases and mutations to data, useful for both memory safety and system design. The Rust programming language implements ownership via the borrow checker, a static analyzer that extends the core type system. The borrow checker is a notorious learning barrier for new Rust users. In this paper, I focus on the gap between understanding ownership in theory versus its implementation in the borrow checker. As a sound and incomplete analysis, compiler errors may arise from either ownership-unsound behavior or limitations of the analyzer. Understanding this distinction is essential for fixing ownership errors. But how are users actually supposed to make the correct inference? Drawing on my experience with using and teaching Rust, I explore the many challenges in interpreting and responding to ownership errors. I also suggest educational and automated interventions that could improve the usability of ownership.
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Rust is a systems programming language that guarantees memory safety without the need for a garbage collector by statically tracking ownership and borrowing events. The associated rules are subtle and unique among industry programming languages, which can make learning Rust more challenging. Motivated by the challenges that Rust learners face, we are developing RustViz, a tool that allows teachers to generate an interactive timeline depicting ownership and borrowing events for each variable in a Rust code example. These visualizations are intended to help Rust learners develop an understanding of ownership and borrowing by example. This paper introduces RustViz by example, shows how teachers can use it to generate visualizations, describes learning goals, and proposes a study designed to evaluate RustViz based on these learning goals.
In recent years, program verifiers and interactive theorem provers have become more powerful and more suitable for verifying large programs or proofs. This has demonstrated the need for improving the user experience of these tools to increase productivity and to make them more accessible to non-experts. This paper presents an integrated development environment for Dafny-a programming language, verifier, and proof assistant-that addresses issues present in most state-of-the-art verifiers: low responsiveness and lack of support for understanding non-obvious verification failures. The paper demonstrates several new features that move the state-of-the-art closer towards a verification environment that can provide verification feedback as the user types and can present more helpful information about the program or failed verifications in a demand-driven and unobtrusive way.
Usability is an increasing concern in open source software (OSS). Given the recent changes in the OSS landscape, it is imperative to examine the OSS contributors current valued factors, practices, and challenges concerning usability. We accumulated this knowledge through a survey with a wide range of contributors to OSS applications. Through analyzing 84 survey responses, we found that many participants recognized the importance of usability. While most relied on issue tracking systems to collect user feedback, a few participants also adopted typical user-centered design methods. However, most participants demonstrated a system-centric rather than a user-centric view. Understanding the diverse needs and consolidating various feedback of end-users posed unique challenges for the OSS contributors when addressing usability in the most recent development context. Our work provided important insights for OSS practitioners and tool designers in exploring ways for promoting a user-centric mindset and improving usability practice in the current OSS communities.
Though statistical analyses are centered on research questions and hypotheses, current statistical analysis tools are not. Users must first translate their hypotheses into specific statistical tests and then perform API calls with functions and parameters. To do so accurately requires that users have statistical expertise. To lower this barrier to valid, replicable statistical analysis, we introduce Tea, a high-level declarative language and runtime system. In Tea, users express their study design, any parametric assumptions, and their hypotheses. Tea compiles these high-level specifications into a constraint satisfaction problem that determines the set of valid statistical tests, and then executes them to test the hypothesis. We evaluate Tea using a suite of statistical analyses drawn from popular tutorials. We show that Tea generally matches the choices of experts while automatically switching to non-parametric tests when parametric assumptions are not met. We simulate the effect of mistakes made by non-expert users and show that Tea automatically avoids both false negatives and false positives that could be produced by the application of incorrect statistical tests.
While visualizations play a crucial role in gaining insights from data, generating useful visualizations from a complex dataset is far from an easy task. Besides understanding the functionality provided by existing visualization libraries, generating the desired visualization also requires reshaping and aggregating the underlying data as well as composing different visual elements to achieve the intended visual narrative. This paper aims to simplify visualization tasks by automatically synthesizing the required program from simple visual sketches provided by the user. Specifically, given an input data set and a visual sketch that demonstrates how to visualize a very small subset of this data, our technique automatically generates a program that can be used to visualize the entire data set. Automating visualization poses several challenges. First, because many visualization tasks require data wrangling in addition to generating plots, we need to decompose the end-to-end synthesis task into two separate sub-problems. Second, because the intermediate specification that results from the decomposition is necessarily imprecise, this makes the data wrangling task particularly challenging in our context. In this paper, we address these problems by developing a new compositional visualization-by-example technique that (a) decomposes the end-to-end task into two different synthesis problems over different DSLs and (b) leverages bi-directional program analysis to deal with the complexity that arises from having an imprecise intermediate specification. We implemented our visualization-by-example algorithm and evaluate it on 83 visualization tasks collected from on-line forums and tutorials. Viser can solve 84% of these benchmarks within a 600 second time limit, and, for those tasks that can be solved, the desired visualization is among the top-5 generated by Viser in 70% of the cases.
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