SPLASH 2020
Sun 15 - Sat 21 November 2020 Online Conference
Fri 20 Nov 2020 11:20 - 11:40 at SPLASH - F-3B
Fri 20 Nov 2020 23:20 - 23:40 at SPLASH - F-3B

Gradual typing provides a methodology to integrate static and dynamic typing, harmonizing their often conflicting advantages in a single language. When a user wants to enjoy the advantages of static typing, most gradual languages require that they add type annotations. Many nontrivial tasks must be undertaken while adding type annotations, including understanding program behaviors and invariants. Unfortunately, if this is done incorrectly then the added type annotations can be wrong–leading to inconsistencies between the program and the type annotations. Gradual typing implementations detect such inconsistencies at runtime, raise cast errors, and generate messages. However, solely relying on such error messages for understanding and fixing inconsistencies and their resulting cast errors is often insufficient for multiple reasons. One reason is that while such messages cover inconsistencies in one execution path, fixing them often requires reconciling information from multiple paths. Another is that users may add many wrong type annotations that they later find difficult to identify and fix, when considering all added annotations.

Recent studies provide evidence that type annotations added during program migration are often wrong and that many programmers prefer compile-time warnings about wrong annotations. Motivated by these results, we develop exploratory typing to help with the static detection, understanding, and fixing of inconsistencies. The key idea of exploratory typing is that it systematically removes dynamic types and explores alternative types for static type annotations that can remedy inconsistencies. To demonstrate the feasibility of exploratory typing, we have implemented it in PyHound, which targets programs written in Reticulated Python, a gradual variant of Python. We have evaluated PyHound on a set of Python programs, and the evaluation results demonstrate that our idea can effectively detect inconsistencies in 98% of the tested programs and fix 93% of inconsistencies, significantly outperforming pytype, a widely used Python tool for enforcing type annotations.

Fri 20 Nov
Times are displayed in time zone: Central Time (US & Canada) change

11:00 - 12:20: F-3BOOPSLA at SPLASH +12h
11:00 - 11:20
Talk
OOPSLA
Shraddha BarkeUniversity of California, San Diego, Hila PelegUniversity of California, San Diego, Nadia PolikarpovaUniversity of California, San Diego
11:20 - 11:40
Talk
OOPSLA
John Peter Campora IIIUniversity of Louisiana at Lafayette, Sheng ChenUniversity of Louisiana at Lafayette
11:40 - 12:00
Talk
OOPSLA
Yu WangDepartment of Computer Science and Technology, Nanjing University, Ke WangVisa Research, Fenjuan GaoNanjing University, Linzhang WangNanjing University
12:00 - 12:20
Talk
OOPSLA
Ifaz KabirUniversity of Alberta, Canada, Yufeng LiUniversity of Waterloo, Ondřej LhotákUniversity of Waterloo
23:00 - 00:20: F-3BOOPSLA at SPLASH
23:00 - 23:20
Talk
OOPSLA
Shraddha BarkeUniversity of California, San Diego, Hila PelegUniversity of California, San Diego, Nadia PolikarpovaUniversity of California, San Diego
23:20 - 23:40
Talk
OOPSLA
John Peter Campora IIIUniversity of Louisiana at Lafayette, Sheng ChenUniversity of Louisiana at Lafayette
23:40 - 00:00
Talk
OOPSLA
Yu WangDepartment of Computer Science and Technology, Nanjing University, Ke WangVisa Research, Fenjuan GaoNanjing University, Linzhang WangNanjing University
00:00 - 00:20
Talk
OOPSLA
Ifaz KabirUniversity of Alberta, Canada, Yufeng LiUniversity of Waterloo, Ondřej LhotákUniversity of Waterloo