Developing enterprise software often requires composing several libraries together with a large body of in-house code. Large APIs introduce a steep learning curve for new developers as a result of their complex object-oriented underpinnings. While the written code in general reflects a programmer’s intent, due to evolutions in an API, code can often become ill-typed, yet still syntactically-correct. Such code fragments will no longer compile, and will need to be updated. We describe an algorithm that automatically repairs such errors, and discuss its application to common problems in software engineering.
Open image in new window

A Type-Directed Approach to Program Repair

Refinement types allow for lightweight program verification by enriching types with logical predicates. Liquid typing provides a decidable refinement inference mechanism that is convenient but subject to two major issues: (1) inference is global and requires top-level annotations, making it unsuitable for inference of modular code components and prohibiting its applicability to library code, and (2) inference failure results in obscure error messages. These difficulties seriously hamper the migration of existing code to use refinements. This paper shows that gradual liquid type inference–a novel combination of liquid inference and gradual refinement types–addresses both issues. Gradual refinement types, which support imprecise predicates that are optimistically interpreted, can be used in argument positions to constrain liquid inference so that the global inference process effectively infers modular specifications usable for library components. Dually, when gradual refinements appear as the result of inference, they signal an inconsistency in the use of static refinements. Because liquid refinements are drawn from a finite set of predicates, in gradual liquid type inference we can enumerate the textitsafe concretizations of each imprecise refinement, i.e., the static refinements that justify why a program is gradually well-typed. This enumeration is useful for static liquid type error explanation, since the safe concretizations exhibit all the potential inconsistencies that lead to static type errors. We develop the theory of gradual liquid type inference and explore its pragmatics in the setting of Liquid Haskell. To demonstrate the utility of our approach, we develop an interactive tool, GuiLT, for gradual liquid type inference in Liquid Haskell that both infers modular types and explores safe concretizations of gradual refinements. We report on the use of GuiLT for error reporting and discuss a case study on the migration of three commonly-used Haskell list manipulation libraries into Liquid Haskell.

https://dl.acm.org/doi/pdf/10.1145/3276502

Gradual liquid type inference

Providing better supports for debugging type errors has been an active research area in the last three decades. Numerous approaches from different perspectives have been developed. Most approaches work well under certain conditions only, for example, when type errors are caused by single leaves and when type annotations are correct. However, the research community is still unaware of which conditions hold in practice and what the real debugging situations look like. We address this problem with a study of 3 program data sets, which were written in different years, using different compilers, and were of diverse sizes. They include more than 55,000 programs, among which more than 2,700 are ill typed. We investigated all the ill-typed programs, and our results indicate that current error debugging support is far from sufficient in practice since only about 35% of all type errors were caused by single leaves. In addition, type annotations cannot always be trusted in error debuggers since about 30% of the time type errors were caused by wrong type annotations. Our study also provides many insights about the debugging behaviors of students in functional programming, which could be exploited for developing more effective error debuggers.

https://dl.acm.org/doi/pdf/10.1145/3133929

How type errors were fixed and what students did

Type inference is convenient by allowing programmers to elide type annotations, but this comes at the cost of often generating very confusing and opaque type error messages that are of little help to fix type errors. Though there have been many successful attempts at making type error messages better in the past thirty years, many classes of errors are still difficult to fix. In particular, current approaches still generate imprecise and uninformative error messages for type errors arising from errors in grouping constructs like parentheses and brackets. Worse, a recent study shows that these errors usually take more than 10 steps to fix and occur quite frequently (around 45% to 60% of all type errors) in programs written by students learning functional programming. We call this class of errors, nonstructural errors. We solve this problem by developing Learnskell, a type error debugger that uses machine learning to help diagnose and deliver high quality error messages, for programs that contain nonstructural errors. While previous approaches usually report type errors on typing constraints or on the type level, Learnskell generates suggestions on the expression level. We have performed an evaluation on more than 1,500 type errors, and the result shows that Learnskell is quite precise. It can correctly capture 86% of all nonstructural errors and locate the error cause with a precision of 63%/87% with the first 1/3 messages, respectively. This is several times more than the precision of state-of-the-art compilers and debuggers. We have also studied the performance of Learnskell and found out that it scales to large programs.

https://dl.acm.org/doi/pdf/10.1145/3133930

Learning user friendly type-error messages

We present FixML, a system for automatically generating feedback on logical errors in functional programming assignments. As functional languages have been gaining popularity, the number of students enrolling functional programming courses has increased significantly. However, the quality of feedback, in particular for logical errors, is hardly satisfying. To provide personalized feedback on logical errors, we present a new error-correction algorithm for functional languages, which combines statistical error-localization and type-directed program synthesis enhanced with components reduction and search space pruning using symbolic execution. We implemented our algorithm in a tool, called FixML, and evaluated it with 497 students’ submissions from 13 exercises, including not only introductory but also more advanced problems. Our experimental results show that our tool effectively corrects various and complex errors: it fixed 43% of the 497 submissions in 5.4 seconds on average and managed to fix a hard-to-find error in a large submission, consisting of 154 lines. We also performed user study with 18 undergraduate students and confirmed that our system actually helps students to better understand their programming errors.

https://dl.acm.org/doi/pdf/10.1145/3276528

Automatic diagnosis and correction of logical errors for functional programming assignments

Automatic type inference is a popular feature of functional programming languages. If a program cannot be typed, the compiler typically reports a single program location in its error message. This ...

/pdf/finding-minimum-type-error-sources-35b3u87izm.pdf

Finding minimum type error sources

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

KingTim

Author Tools

Chat about Author

Papers

Finding minimum type error sources