相关论文: Making Python Code Idiomatic by Automatic Refactor…

Producing high-quality code is essential as it makes a codebase more maintainable, reducing the cost and effort associated with a project. However, students learning to program are often given short, automatically graded programming tasks…

The rapid evolution of software libraries poses a considerable hurdle for code generation, necessitating continuous adaptation to frequent version updates while preserving backward compatibility. While existing code evolution benchmarks…

Pattern matching is a powerful tool for symbolic computations. Applications include term rewriting systems, as well as the manipulation of symbolic expressions, abstract syntax trees, and XML and JSON data. It also allows for an intuitive…

Code generation models based on the pre-training and fine-tuning paradigm have been increasingly attempted by both academia and industry, resulting in well-known industrial models such as Codex, CodeGen, and PanGu-Coder. To evaluate the…

To create unit tests, it may be necessary to refactor the production code, e.g. by widening access to specific methods or by decomposing classes into smaller units that are easier to test independently. We report on an extensive study to…

The lexical and syntactic disparities among different programming languages (e.g., Java and Python) pose significant challenges for multi-language software engineering tasks such as cross-language code clone detection and code retrieval,…

Large Language Models (LLMs) have become a popular choice for many Natural Language Processing (NLP) tasks due to their versatility and ability to produce high-quality results. Specifically, they are increasingly used for automatic code…

Software developers use Application Programming Interfaces (APIs) of libraries and frameworks extensively while writing programs. In this context, the recommendations provided in code completion pop-ups help developers choose the desired…

Compile-time information flow analysis has been a promising technique for protecting confidentiality and integrity of private data. In the last couple of decades, a large number of information flow security tools in the form of run-time…

Nowadays, developers often reuse existing APIs to implement their programming tasks. A lot of API usage patterns are mined to help developers learn API usage rules. However, there are still many missing variables to be synthesized when…

Blindspots in APIs can cause software engineers to introduce vulnerabilities, but such blindspots are, unfortunately, common. We study the effect APIs with blindspots have on developers in two languages by replicating an 109-developer,…

Code refactoring is a fundamental software engineering practice aimed at improving code quality and maintainability. Despite its importance, developers often neglect refactoring due to the significant time, effort, and resources it…

Python is a popular high-level general-purpose programming language also heavily used by the scientific community. It supports a variety of different programming paradigms and is preferred by many for its ease of use. With the vision of…

A great part of software development involves conceptualizing or communicating the underlying procedures and logic that needs to be expressed in programs. One major difficulty of programming is turning concept into code, especially when…

Pseudo code is one of the valuable artifacts to comprehending the complex program codes. Most of the source code still has no equivalent pseudo code, due to the time-consuming process of writing pseudo codes. In this work, we have developed…

While hardware generators have drastically improved design productivity, they have introduced new challenges for the task of verification. To effectively cover the functionality of a sophisticated generator, verification engineers require…

Python type annotations enable static type checking, but most code remains untyped because manual annotation is time-consuming and tedious. Past approaches to automatic type inference fall short: static methods struggle with dynamic…

Context: Gradually-typed languages allow typed and untyped code to interoperate, but typically come with significant drawbacks. In some languages, the types are unreliable; in others, communication across type boundaries can be extremely…

This study aims to enhance the maintainability of code generated by Large Language Models (LLMs), with a focus on the Python programming language. As the use of LLMs for coding assistance grows, so do concerns about the maintainability of…

Code translation, the automatic conversion of programs between languages, is a growing use case for Large Language Models (LLMs). However, direct one-shot translation often fails to preserve program intent, leading to errors in control…