用于调试类型错误的反事实输入

Sheng Chen, Martin Erwig
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引用次数: 65

摘要

在现代软件开发中,修改程序以响应类型错误扮演着重要的角色。然而,对于高表现力的类型系统来说,生成良好的类型错误消息仍然是一个问题。现有的方法在定位错误和提出补救措施方面往往缺乏精确性。具体来说,它们要么无法一致地定位类型错误的来源,要么报告了太多潜在的错误位置。此外,提供的更改建议往往是不正确的。这使得调试过程冗长而无效。我们提出了一种解决类型调试问题的方法,该方法基于生成和过滤一组全面的类型更改建议。具体来说,我们生成所有可能修复类型错误的(保留程序结构的)类型更改。这些建议将被排序,并以迭代的方式呈现给程序员。在某些情况下,我们还会提出修改程序的建议。在大多数情况下,这种策略可以快速地交付正确的变更建议,同时不会遗漏任何罕见的建议。潜在的大量类型更改建议的计算是高效的,因为它基于一种变分类型推断算法,该算法只对具有变化的程序进行一次类型检查,从而有效地重用共享部分的类型信息。我们已经评估了我们的方法,并与以前的方法进行了比较。基于从文献中提取的大量示例,我们发现我们的方法优于其他方法,并提供了一个可行的替代方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Counter-factual typing for debugging type errors
Changing a program in response to a type error plays an important part in modern software development. However, the generation of good type error messages remains a problem for highly expressive type systems. Existing approaches often suffer from a lack of precision in locating errors and proposing remedies. Specifically, they either fail to locate the source of the type error consistently, or they report too many potential error locations. Moreover, the change suggestions offered are often incorrect. This makes the debugging process tedious and ineffective. We present an approach to the problem of type debugging that is based on generating and filtering a comprehensive set of type-change suggestions. Specifically, we generate all (program-structure-preserving) type changes that can possibly fix the type error. These suggestions will be ranked and presented to the programmer in an iterative fashion. In some cases we also produce suggestions to change the program. In most situations, this strategy delivers the correct change suggestions quickly, and at the same time never misses any rare suggestions. The computation of the potentially huge set of type-change suggestions is efficient since it is based on a variational type inference algorithm that type checks a program with variations only once, efficiently reusing type information for shared parts. We have evaluated our method and compared it with previous approaches. Based on a large set of examples drawn from the literature, we have found that our method outperforms other approaches and provides a viable alternative.
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