Related papers: Debugging Functional Programs by Interpretation

Automated debugging techniques have the potential to reduce developer effort in debugging, and have matured enough to be adopted by industry. However, one critical issue with existing techniques is that, while developers want rationales for…

Binary decompilation aims to recover binaries into high-level source code, but existing evaluations mainly rely on syntactic similarity or single-axis readability metrics, which fail to capture practical reusability. We propose a…

Creating functions is at the center of writing computer programs. But there has been little empirical research on how this is done and what are the considerations that developers use. We design an experiment in which we can compare the…

Probabilistic programming is a growing area that strives to make statistical analysis more accessible, by separating probabilistic modelling from probabilistic inference. In practice this decoupling is difficult. No single inference…

Formula-based debugging techniques are becoming increasingly popular, as they provide a principled way to identify potentially faulty statements together with information that can help fix such statements. Although effective, these…

Fault localization is a fundamental aspect of debugging, aiming to identify code regions likely responsible for failures. Traditional techniques primarily correlate statement execution with failures, yet program behavior is influenced by…

Due to the increased complexity of parallel and distributed programs, debugging of them is considered to be the most difficult and time consuming part of the software lifecycle. Tool support is hence a crucial necessity to hide complexity…

As most parallel and distributed programs are internally non-deterministic -- consecutive runs with the same input might result in a different program flow -- vanilla cyclic debugging techniques as such are useless. In order to use cyclic…

Program errors are hard to find because of the cause-effect gap between the time when an error occurs and the time when the error becomes apparent to the programmer. Although debugging techniques such as conditional and data breakpoints…

Identifying and resolving logic errors can be one of the most frustrating challenges for novices programmers. Unlike syntax errors, for which a compiler or interpreter can issue a message, logic errors can be subtle. In certain conditions,…

We present an approach to program reasoning which inserts between a program and its verification conditions an additional layer, the denotation of the program expressed in a declarative form. The program is first translated into its…

Debugging denotes the process of detecting root causes of unexpected observable behaviors in programs, such as a program crash, an unexpected output value being produced or an assertion violation. Debugging of program errors is a difficult…

While functional programming is an efficient way to express complex software, functional programming languages have a steep learning curve. Haskell can be challenging to learn for students who were only introduced to imperative programming.…

Large language models (LLMs) are trained through multi-stage pipelines over heterogeneous data sources, yet developers lack a principled way to pinpoint the specific data responsible for an observed behavior. This lack of observability…

Deductive verification is an effective method to ensure that a given system exposes the intended behavior. In spite of its proven usefulness and feasibility in selected projects, deductive verification is still not a mainstream technique.…

We describe a system that simplifies the process of debugging programs produced by computer-aided parallelization tools. The system uses relative debugging techniques to compare serial and parallel executions in order to show where the…

In recent years, compositional symbolic execution (CSE) tools have been growing in prominence and are becoming more and more applicable to real-world codebases. Still to this day, however, debugging the output of these tools remains…

Introductory programming courses often rely on small code-writing exercises that have clearly specified problem statements. This limits opportunities for students to practice how to clarify ambiguous requirements -- a critical skill in…

Visual programming, a modular and generalizable paradigm, integrates different modules and Python operators to solve various vision-language tasks. Unlike end-to-end models that need task-specific data, it advances in performing visual…

Large language models (LLMs) have made significant progress in code generation tasks, but their performance in tackling programming problems with complex data structures and algorithms remains suboptimal. To address this issue, we propose…