The behavior of gradual types: a user study

Wilson, Preston Tunnell; Greenman, Ben; Pombrio, Justin; Krishnamurthi, Shriram

doi:10.1145/3393673.3276947

Cited by 4 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of studies employed workers in order to identify preferences and consensus; for example, researchers have sought to identify preferences about the order of writing expressions such as new byte [10 + length] and new byte[length + 10] [4], about gradual typing semantics [34], and about specific features in a novel programming language [35].…”

Section: Surveysmentioning

confidence: 99%

See 1 more Smart Citation

Crowdsourcing in Computing Education Research: Case Amazon MTurk

Hellas

Zavgorodniaia

Sorva

2020

Koli Calling '20: Proceedings of the 20th Koli Calling International Conference on Computing Education Research

View full text Add to dashboard Cite

Crowdsourcing platforms such as Amazon MTurk provide access to a human workforce that can be given tasks to complete online for a fee. In this article, we review studies in computing education research (CER) that rely on crowdsourcing; we also describe our own experiences of using Amazon MTurk for a CER study. We discuss challenges in recruiting workers with specific backgrounds-such as no programming experience-and considerations in filtering out unreliable research participants. Combining recommendations from the literature with the lessons that we learned whilst conducting our study, we synthesize advice for researchers in CER who are considering crowdsourcing. In our case study, we did not find widespread foul play by crowdsourced workers and, overall, our experiences and the literature suggest that crowdsourced CER is feasible. It is, however, uncertain to what extent crowdsourced data can produce answers that apply to specific educational contexts. More research is needed before definitive conclusions can be drawn about the validity and generalizability of crowdsourced CER. CCS CONCEPTS• Social and professional topics → Computing education.

show abstract

Section: Surveysmentioning

confidence: 99%

“…Others simply used MTurk workers as an additional population (e.g. [12,34]). Some studies have focused solely on MTurk workers.…”

Section: Study Designs and Data Qualitymentioning

confidence: 99%

Crowdsourcing in Computing Education Research: Case Amazon MTurk

Hellas

Zavgorodniaia

Sorva

2020

Koli Calling '20: Proceedings of the 20th Koli Calling International Conference on Computing Education Research

View full text Add to dashboard Cite

show abstract

“…Greenman et al take a more functional approach, again modelling erasure, transient ("first order"), and behavioural ("higher order") semantics [28], and also present performance information based on Typed Racket. Wilson et al take a rather different approach, employing questionnaires to investigate the semantics programmers expect of a gradual typing system [60].…”

Section: Related Workmentioning

confidence: 99%

Transient Typechecks are (Almost) Free

Marr¹,

Homer²,

Noble³

2018

Preprint

View full text Add to dashboard Cite

Transient gradual typing imposes run-time type tests that typically cause a linear slowdown in programs' performance. This performance impact discourages the use of type annotations because adding types to a program makes the program slower. A virtual machine can employ standard justin-time optimizations to reduce the overhead of transient checks to near zero. These optimizations can give gradually-typed languages performance comparable to state-of-the-art dynamic languages, so programmers can add types to their code without affecting their programs' performance. ACM Subject ClassificationSoftware and its engineering → Just-in-time compilers; Software and its engineering → Object oriented languages; Software and its engineering → Interpreters

show abstract