Toward general diagnosis of static errors

Zhang, Danfeng; Myers, Andrew C.

doi:10.1145/2535838.2535870

Cited by 36 publications

(36 citation statements)

References 31 publications

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“…Instead they use Haack and Wells' approach [25,26] of duplicating constraint sets when dealing with let-expressions, which, as mentioned above, could lead to an exponential growth of the number of generated constraints. The authors acknowledge this issue and report that they "find performance is still reasonable with this approach" [67]. They then showed that their method scales up to more expressive type systems such as Haskell's type system [68].…”

Section: Significant Non-slicing Type Explanation Methodsmentioning

confidence: 99%

“…Many approaches use this idea to improve error reporting. A probably incomplete list includes [41,25,26,23,33,32,27,34,29,52,53,54,50,44,67,68]. Independently from this separation, there exist other approaches toward improving errors [66]: error explanation systems [8,20,19,65] which focus on explaining the reasoning steps leading to a type error, and error reporting systems [60,57,13] which focus on trying to precisely locate errors in pieces of code.…”

Section: Introductionmentioning

confidence: 99%

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Skalpel: A constraint-based type error slicer for Standard ML

Rahli

Wells

Pirie

et al. 2017

Journal of Symbolic Computation

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Compilers for languages with type inference algorithms often produce confusing type error messages and give a single error location which is sometimes far away from the real location of the error. Attempts at solving this problem often (1) fail to include the multiple program points which make up the type error; (2) report tree fragments which do not correspond to any place in the user program; and (3) give incorrect type information/diagnosis which can be highly confusing. We present Skalpel, a type error slicing tool which solves these problems by giving the programmer all and only the information involved with a type error to significantly aid in diagnosis and repair of type errors. Skalpel relies on a simple and general constraint system, a sophisticated constraint generator which is linear in program size, and a constraint solver which is terminating. Skalpel's constraint system can elegantly and efficiently handle intricate features such as SML's open. We also show that the Skalpel tool is general enough to deal not only with one source code file and one single error, but highlights all and only the possible locations of the error(s) in all affected files and produces all the culprit multiple program slices.

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Section: Significant Non-slicing Type Explanation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Skalpel: A constraint-based type error slicer for Standard ML

Rahli

Wells

Pirie

et al. 2017

Journal of Symbolic Computation

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“…The former is mostly standard (e.g., for Hindley/Milner system (Wand 1987;Aiken and Wimmers 1993;Zhang and Myers 2014)), hence omitted in this paper. Next, we assume all base types are available, and focus on the inference of the distance counterpart.…”

Section: Type Inferencementioning

confidence: 99%

LightDP: towards automating differential privacy proofs

ZhangDanfeng

KiferDaniel

2017

SIGPLAN Not.

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The growing popularity and adoption of differential privacy in academic and industrial settings has resulted in the development of increasingly sophisticated algorithms for releasing information while preserving privacy. Accompanying this phenomenon is the natural rise in the development and publication of incorrect algorithms, thus demonstrating the necessity of formal verification tools. However, existing formal methods for differential privacy face a dilemma: methods based on customized logics can verify sophisticated algorithms but come with a steep learning curve and significant annotation burden on the programmers, while existing programming platforms lack expressive power for some sophisticated algorithms.In this paper, we present LightDP, a simple imperative language that strikes a better balance between expressive power and usability. The core of LightDP is a novel relational type system that separates relational reasoning from privacy budget calculations. With dependent types, the type system is powerful enough to verify sophisticated algorithms where the composition theorem falls short. In addition, the inference engine of LightDP infers most of the proof details, and even searches for the proof with minimal privacy cost bound when multiple proofs exist. We show that LightDP verifies sophisticated algorithms with little manual effort.

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“…Techniques that improve type-error messages [19], [24], [25], [45] can enable CASCADE to infer better changes for more type-errors.…”

Section: Type-error Messagesmentioning

confidence: 99%

Cascade: A Universal Programmer-Assisted Type Qualifier Inference Tool

Vakilian¹,

Phaosawasdi²,

Ernst³

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

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Abstract-Type qualifier inference tools usually operate in batch mode and assume that the program must not be changed except to add the type qualifiers. In practice, programs must be changed to make them type-correct, and programmers must understand them. CASCADE is an interactive type qualifier inference tool that is easy to implement and universal (i.e., it can work for any type qualifier system for which a checker is implemented). It shows that qualifier inference can achieve better results by involving programmers rather than relying solely on automation.

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Toward general diagnosis of static errors

Cited by 36 publications

References 31 publications

Skalpel: A constraint-based type error slicer for Standard ML

Skalpel: A constraint-based type error slicer for Standard ML

LightDP: towards automating differential privacy proofs

Cascade: A Universal Programmer-Assisted Type Qualifier Inference Tool

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