Error Localization for Sequential Effect Systems (Extended Version)
July 28, 2023 Β· Declared Dead Β· π Sensors Applications Symposium
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Colin S. Gordon, Chaewon Yun
arXiv ID
2307.15777
Category
cs.PL: Programming Languages
Citations
1
Venue
Sensors Applications Symposium
Last Checked
4 months ago
Abstract
We describe a new concrete approach to giving predictable error locations for sequential (flow-sensitive) effect systems. Prior implementations of sequential effect systems rely on either computing a bottom-up effect and comparing it to a declaration (e.g., method annotation) or leaning on constraint-based type inference. These approaches do not necessarily report program locations that precisely indicate where a program may "go wrong" at runtime. Instead of relying on constraint solving, we draw on the notion of a residual from literature on ordered algebraic structures. Applying these to effect quantales (a large class of sequential effect systems) yields an implementation approach which accepts exactly the same program as an original effect quantale, but for effect-incorrect programs is guaranteed to fail type-checking with predictable error locations tied to evaluation order. We have implemented this idea in a generic effect system implementation framework for Java, and report on experiences applying effect systems from the literature and novel effect systems to Java programs. We find that the reported error locations with our technique are significantly closer to the program points that lead to failed effect checks.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted