26,098 papers · page 90 of 1,305
Abstract Experience in teaching functional programming (FP) on a relational basis has led the author to focus on a graphical style of expression and reasoning in which a geometric construct shines: the (semi) commutative square. In the classroom this is termed the “magic square” …
Abstract Algebraic effects and handlers is an increasingly popular paradigm for programming with effects. A key benefit is modularity: programs with effects are defined against an interface of operations, allowing the implementation of effects to be defined and refined without ch…
Abstract Where dual-numbers forward-mode automatic differentiation (AD) pairs each scalar value with its tangent value, dual-numbers reverse-mode AD attempts to achieve reverse AD using a similarly simple idea: by pairing each scalar value with a backpropagator function. Its corr…
Abstract This paper explores a principled approach to calculating abstract machines and associated compilers, starting from an intrinsically typed interpreter. After deriving a compiler for a simple expression language in some detail, the first steps of this calculation are repea…
Abstract Good test-suites are an important tool to check the correctness of programs. They are also essential in unsupervised educational settings, like automatic grading or for students to check their solution to some programming task by themselves. For most Haskell programming …
Abstract Fenwick trees , also known as binary indexed trees are a clever solution to the problem of maintaining a sequence of values while allowing both updates and range queries in sublinear time. Their implementation is concise and efficient—but also somewhat baffling, consisti…
Review of "Haskell in Depth" by
Abstract Recursive types and bounded quantification are prominent features in many modern programming languages, such as Java, C#, Scala, or TypeScript. Unfortunately, the interaction between recursive types, bounded quantification, and subtyping has shown to be problematic in th…
Accurate method naming is crucial for code readability and maintainability. However, manually creating concise and meaningful names remains a significant challenge. To this end, in this paper, we propose an approach based on Large Language Model (LLMs) to suggest method names acc…
Nowadays, developers increasingly rely on solutions powered by Large Language Models (LLM) to assist them with their coding tasks. This makes it crucial to align these tools with human values to prevent malicious misuse. In this paper, we propose a comprehensive framework for ass…
Large Language Models (LLMs) were shown to struggle with long-term planning, which may be caused by the limited way in which they explore the space of possible solutions. We propose an architecture where a Reinforcement Learning (RL) Agent guides an LLM’s space exploration: (1) t…
Adaptive Random Testing (ART) has faced criticism, particularly for its computational inefficiency, as highlighted by Arcuri and Briand. Their analysis clarified how ART requires a quadratic number of distance computations as the number of test executions increases, which limits …
Debugging consists in understanding the behavior of a program to identify and correct its defects. Breakpoints are the most commonly used debugging tool and aim to facilitate the debugging process by allowing developers to interrupt a program’s execution at a source code location…
Software development Effort Estimation (SEE) comprises predicting the most realistic amount of effort (e.g., in work hours) required to develop or maintain software based on incomplete, uncertain, and noisy input. Expert judgment is the dominant SEE strategy used in the industry.…
Model-based testing (MBT) has been an important methodology in software engineering, attracting extensive research attention for over four decades. However, despite its academic acclaim, studies examining the impact of MBT in industrial environments—particularly regarding its ext…
Detecting semantically recurring vulnerabilities with similar root causes remains a challenge due to the complex interactions between multiple variables. This paper introduces V ul PA, a novel approach for precisely identifying such vulnerabilities through complex inter-procedura…
Multiple machine learning (ML) models are often incorporated into real-world ML systems. However, updating an individual model in these ML systems frequently results in regression errors, where the new model performs worse than the old model for some inputs. While model-level reg…
In the realm of natural language processing (NLP), the rising computational demands of modern models bring energy efficiency to the forefront of sustainable computing. Preprocessing tasks, such as tokenization, stemming, and POS tagging, are critical steps in transforming raw tex…
Programming is an essential activity in data science (DS). Unlike regular software developers, DS programmers often use Jupyter notebooks instead of conventional IDEs. Moreover, DS programmers focus on statistics, data analytics, and modeling rather than writing production-ready …
The compiler bug duplication problem (where many test failures are caused by the same compiler bug) can lead to huge waste of time and resource in diagnosing test failures produced by compiler testing. It is particularly challenging with regard to the silent compiler bugs that do…