Taming Type Annotations in Gradual Typing

被引:6
作者
Campora, John Peter [1 ]
Chen, Sheng [1 ]
机构
[1] Univ Louisiana Lafayette, CACS, Lafayette, LA 70504 USA
来源
PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL | 2020年 / 4卷 / 04期
基金
美国国家科学基金会;
关键词
gradual typing; case errors; variational Types; INFERENCE;
D O I
10.1145/3428259
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
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.
引用
收藏
页数:30
相关论文
共 57 条
  • [1] DYNAMIC TYPING IN A STATICALLY TYPED LANGUAGE
    ABADI, M
    CARDELLI, L
    PIERCE, B
    PLOTKIN, G
    [J]. ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS, 1991, 13 (02): : 237 - 268
  • [2] Ahmed A, 2017, P ACM PROGRAM LANG, V1, DOI 10.1145/3110283
  • [3] Blame for All
    Ahmed, Amal
    Findler, Robert Bruce
    Siek, Jeremy G.
    Wadler, Philip
    [J]. ACM SIGPLAN NOTICES, 2011, 46 (01) : 201 - 214
  • [4] Aiken Alexander., 1991, POPL 91, P279, DOI [DOI 10.1145/99583.99621, 10.1145/99583.99621]
  • [5] [Anonymous], 2006, Scheme and Functional Programming
  • [6] Gradual Program Verification
    Bader, Johannes
    Aldrich, Jonathan
    Tanter, Eric
    [J]. VERIFICATION, MODEL CHECKING, AND ABSTRACT INTERPRETATION (VMCAI 2018), 2018, 10747 : 25 - 46
  • [7] Schwerter FB, 2014, ICFP'14: PROCEEDINGS OF THE 2014 ACM SIGPLAN INTERNATIONAL CONFERENCE ON FUNCTIONAL PROGRAMMING, P283, DOI 10.1145/2628136.2628149
  • [8] Brassel B., 2004, INT WORKSH IMPL FUNC
  • [9] Campora John, 2018, P 45 ACM SIGPLAN S P
  • [10] Campora John Peter, 2018, P ACM PROGR LANG, DOI [10.1145/3236793, DOI 10.1145/3236793]