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Rust is a young systems programming language designed to provide both the safety guarantees of high-level languages and the execution performance of low-level languages. To achieve this design goal, Rust provides a suite of safety rules and checks against those rules at the compile time to eliminate many memory-safety and thread-safety issues. Due to its safety and performance, Rust’s popularity has increased significantly in recent years, and it has already been adopted to build many safety-critical software systems. It is critical to understand the learning and programming challenges imposed by Rust’s safety rules. For this purpose, we first conducted an empirical study through close, manual inspection of 100 Rust-related Stack Overflow questions. We sought to understand (1) what safety rules are challenging to learn and program with, (2) under which contexts a safety rule becomes more difficult to apply, and (3) whether the Rust compiler is sufficiently helpful in debugging safety-rule violations. We then performed an online survey with 101 Rust programmers to validate the findings of the empirical study. We invited participants to evaluate program variants that differ from each other, either in terms of violated safety rules or the code constructs involved in the violation, and compared the participants’ performance on the variants. Our mixed-methods investigation revealed a range of consistent findings that can benefit Rust learners, practitioners, and language designers.
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Profiling Programming Language Learning
This paper documents a year-long experiment to “profile” the process of learning a programming language: gathering data to understand what makes a language hard to learn, and using that data to improve the learning process. We added interactive quizzes to The Rust Programming Language, the official textbook for learning Rust. Over 13 months, 62,526 readers answered questions 1,140,202 times. First, we analyze the trajectories of readers. We find that many readers drop-out of the book early when faced with difficult language concepts like Rust’s ownership types. Second, we use classical test theory and item response theory to analyze the characteristics of quiz questions. We find that better questions are more conceptual in nature, such as asking why a program does not compile vs. whether a program compiles. Third, we performed 12 interventions into the book to help readers with difficult questions. We find that on average, interventions improved quiz scores on the targeted questions by +20
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- Award ID(s):
- 2319014
- PAR ID:
- 10539093
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Programming Languages
- Volume:
- 8
- Issue:
- OOPSLA1
- ISSN:
- 2475-1421
- Page Range / eLocation ID:
- 29 to 54
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Upon encountering this publication, one might ask the obvious question, "Why do we need another deep learning and natural language processing book?" Several excellent ones have been published, covering both theoretical and practical aspects of deep learning and its application to language processing. However, from our experience teaching courses on natural language processing, we argue that, despite their excellent quality, most of these books do not target their most likely readers. The intended reader of this book is one who is skilled in a domain other than machine learning and natural language processing and whose work relies, at least partially, on the automated analysis of large amounts of data, especially textual data. Such experts may include social scientists, political scientists, biomedical scientists, and even computer scientists and computational linguists with limited exposure to machine learning. Existing deep learning and natural language processing books generally fall into two camps. The first camp focuses on the theoretical foundations of deep learning. This is certainly useful to the aforementioned readers, as one should understand the theoretical aspects of a tool before using it. However, these books tend to assume the typical background of a machine learning researcher and, as a consequence, I have often seen students who do not have this background rapidly get lost in such material. To mitigate this issue, the second type of book that exists today focuses on the machine learning practitioner; that is, on how to use deep learning software, with minimal attention paid to the theoretical aspects. We argue that focusing on practical aspects is similarly necessary but not sufficient. Considering that deep learning frameworks and libraries have gotten fairly complex, the chance of misusing them due to theoretical misunderstandings is high. We have commonly seen this problem in our courses, too. This book, therefore, aims to bridge the theoretical and practical aspects of deep learning for natural language processing. We cover the necessary theoretical background and assume minimal machine learning background from the reader. Our aim is that anyone who took introductory linear algebra and calculus courses will be able to follow the theoretical material. To address practical aspects, this book includes pseudo code for the simpler algorithms discussed and actual Python code for the more complicated architectures. The code should be understandable by anyone who has taken a Python programming course. After reading this book, we expect that the reader will have the necessary foundation to immediately begin building real-world, practical natural language processing systems, and to expand their knowledge by reading research publications on these topics. https://doi.org/10.1017/9781009026222more » « less
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Sosnovsky, S.; Brusilovsky, P; Baraniuk, R. G.; Lan, A. S. (Ed.)As students read textbooks, they often highlight the material they deem to be most important. We analyze students’ highlights to predict their subsequent performance on quiz questions. Past research in this area has encoded highlights in terms of where the highlights appear in the stream of text—a positional representation. In this work, we construct a semantic representation based on a state-of-the-art deep-learning sentence embedding technique (SBERT) that captures the content-based similarity between quiz questions and highlighted (as well as non-highlighted) sentences in the text. We construct regression models that include latent variables for student skill level and question difficulty and augment the models with highlighting features. We find that highlighting features reliably boost model performance. We conduct experiments that validate models on held-out questions, students, and student-questions and find strong generalization for the latter two but not for held-out questions. Surprisingly, highlighting features improve models for questions at all levels of the Bloom taxonomy, from straightforward recall questions to inferential synthesis/evaluation/creation questions.more » « less
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null (Ed.)As students read textbooks, they often highlight the material they deem to be most important. We analyze students’ highlights to predict their subsequent performance on quiz questions. Past research in this area has encoded highlights in terms of where the highlights appear in the stream of text—a positional representation. In this work, we construct a semantic representation based on a state-of-the-art deep-learning sentence embedding technique (SBERT) that captures the content-based similarity between quiz questions and highlighted (as well as non-highlighted) sentences in the text. We construct regression models that include latent variables for student skill level and question difficulty and augment the models with highlighting features. We find that highlighting features reliably boost model performance. We conduct experiments that validate models on held-out questions, students, and student-questions and find strong generalization for the latter two but not for held-out questions. Surprisingly, highlighting features improve models for questions at all levels of the Bloom taxonomy, from straightforward recall questions to inferential synthesis/evaluation/creation questions.more » « less
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Programmers learning Rust struggle to understand ownership types, Rust’s core mechanism for ensuring memory safety without garbage collection. This paper describes our attempt to systematically design a pedagogy for ownership types. First, we studied Rust developers’ misconceptions of ownership to create the Ownership Inventory, a new instrument for measuring a person’s knowledge of ownership. We found that Rust learners could not connect Rust’s static and dynamic semantics, such as determining why an ill-typed program would (or would not) exhibit undefined behavior. Second, we created a conceptual model of Rust’s semantics that explains borrow checking in terms of flow-sensitive permissions on paths into memory. Third, we implemented a Rust compiler plugin that visualizes programs under the model. Fourth, we integrated the permissions model and visualizations into a broader pedagogy of ownership by writing a new ownership chapter forThe Rust Programming Language, a popular Rust textbook. Fifth, we evaluated an initial deployment of our pedagogy against the original version, using reader responses to the Ownership Inventory as a point of comparison. Thus far, the new pedagogy has improved learner scores on the Ownership Inventory by an average of 9more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3649812
