Interactive Extraction of Examples from Existing Code

Head, Andrew; Glassman, Elena L.; Hartmann, Björn; Hearst, Marti A.

doi:10.1145/3173574.3173659

Cited by 20 publications

(11 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, we are excited by the possibilities created by having a practical information flow analysis that can run today on any Rust program. Many exciting systems for tasks like debugging [20], example generation [17], and program repair [36] rely on information flow in some form, and we hope that Flowistry can support the development of these tools.…”

Section: Discussionmentioning

confidence: 99%

Modular Information Flow through Ownership

Crichton,

Patrignani,

Agrawala

et al. 2021

Preprint

View full text Add to dashboard Cite

Statically analyzing information flow, or how data influences other data within a program, is a challenging task in imperative languages. Analyzing pointers and mutations requires access to a program's complete source. However, programs often use pre-compiled dependencies where only type signatures are available. We demonstrate that ownership types can be used to soundly and precisely analyze information flow through function calls given only their type signature. From this insight, we built Flowistry, a system for analyzing information flow in Rust, an ownership-based language. We prove the system's soundness as a form of noninterference using the Oxide formal model of Rust. Then we empirically evaluate the precision of Flowistry, showing that modular flows are identical to whole-program flows in 90% of cases drawn from large Rust codebases. We illustrate the applicability of Flowistry by implementing both a program slicer and an IFC checker on top of it.

show abstract

Section: Discussionmentioning

confidence: 99%

Modular Information Flow through Ownership

Crichton,

Patrignani,

Agrawala

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Several projects highlight the value in extracting and analyzing existing designs from the web, such as website designs [32,44], visualization designs [9,11,22,23,26,46], and even code structure [24]. Online platforms such as bl.ocks.org 1 and Many Eyes [58] also make it easier for user communities to engage with visualizations online.…”

Section: Visualization Mining Modeling and Analysismentioning

confidence: 99%

Exploring D3 Implementation Challenges on Stack Overflow

Battle¹,

Feng²,

Webber³

2021

Preprint

View full text Add to dashboard Cite

Visualization languages help to standardize the process of designing effective visualizations, one of the most prominent being D3.js.However, few researchers have analyzed at scale how users incorporate these languages into existing visualization implementation processes, i.e., implementation workflows. In this paper, we present a new method for evaluating visualization languages. Our method emphasizes the experiences of users as observed through the online communities that have sprouted to facilitate public discussion and support around visualization languages. We demonstrate our method by analyzing D3 implementation workflows and challenges discussed on Stack Overflow. Our results show how the visualization community may be limiting its understanding of users' visualization implementation challenges by ignoring the larger context in which languages such as D3 are used. Based on our findings, we suggest new research directions to enhance the user experience with visualization languages. All our data and code are

show abstract

“…CodeMend [27] is a system that supports finding and integration of code, which leverages a neural embedding model to jointly model natural language and code. CodeScoop [28] helps developers extract code fragments in their current projects for further code reuse. Examplore [29] is the state-of-art work for visualizing hundreds of examples for a given API element.…”

Section: B Recommending Api Usage Examplesmentioning

confidence: 99%

Comprehensive Integration of API Usage Patterns

Shen

Zou

et al. 2021

Preprint

View full text Add to dashboard Cite

Nowadays, developers often reuse existing APIs to implement their programming tasks. A lot of API usage patterns are mined to help developers learn API usage rules. However, there are still many missing variables to be synthesized when developers integrate the patterns into their programming context. To deal with this issue, we propose a comprehensive approach to integrate API usage patterns in this paper. We first perform an empirical study by analyzing how API usage patterns are integrated in real-world projects. We find the expressions for variable synthesis is often non-trivial and can be divided into 5 syntax types. Based on the observation, we promote an approach to help developers interactively complete API usage patterns. Compared to the existing code completion techniques, our approach can recommend infrequent expressions accompanied with their real-world usage examples according to the user intent. The evaluation shows that our approach could assist users to integrate APIs more efficiently and complete the programming tasks faster than existing works.

show abstract

Interactive Extraction of Examples from Existing Code

Cited by 20 publications

References 31 publications

Modular Information Flow through Ownership

Modular Information Flow through Ownership

Exploring D3 Implementation Challenges on Stack Overflow

Comprehensive Integration of API Usage Patterns

Contact Info

Product

Resources

About