相关论文: Simple and Effective Type Check Removal through La…

JavaScript (JS) is one of the most popular programming languages, and widely used for web apps and even backend development. Due to its dynamic nature, however, JS applications often have a reputation for poor software quality. As a…

Large language models (LLMs) are widely used in software development. However, the code generated by LLMs often contains vulnerabilities. Several secure code generation methods have been proposed to address this issue, but their current…

Dynamic languages are praised for their flexibility and expressiveness, but static analysis often yields many false positives and verification is cumbersome for lack of structure. Hence, unit testing is the prevalent incomplete method for…

Parser generators generate translators from language specifications. In many cases, such specifications contain semantic actions written in the same language as the generated code. Since these actions are subject to little static checking,…

Code completion is a key feature of Integrated Development Environments (IDEs), aimed at predicting the next tokens a developer is likely to write, helping them write code faster and with less effort. Modern code completion approaches are…

JavaScript implementations are tested for conformance to the ECMAScript standard using a large hand-written test suite. Not only in this a tedious approach, it also relies solely on the natural language specification for differentiating…

Dynamic languages (such as Python and JavaScript) offer flexibility and simplified type handling for programming, but this can also lead to an increase in type-related errors and additional overhead for compile-time type inference. As a…

Testing has become an indispensable activity of software development, yet writing good and relevant tests remains a quite challenging task. One well-known problem is that it often is impossible or unrealistic to test for every outcome, as…

Elixir is a functional programming language with dynamic typing. We propose a gradual type system that makes it possible to perform type-checking on a significant fragment of the language. An important feature of the type system is that it…

Gradual typing combines static and dynamic typing in the same program. One would hope that the performance in a gradually typed language would range between that of a dynamically typed language and a statically typed language. Existing…

JavaScript (JS) is a popular, platform-independent programming language. To ensure the interoperability of JS programs across different platforms, the implementation of a JS engine should conform to the ECMAScript standard. However, doing…

Code analyzers such as Error Prone and FindBugs detect code patterns symptomatic of bugs, performance issues, or bad style. These tools express patterns as quick fixes that detect and rewrite unwanted code. However, it is difficult to come…

Programs expecting structured inputs often consist of both a syntactic analysis stage, which parses raw input, and a semantic analysis stage, which conducts checks on the parsed input and executes the core logic of the program.…

Large language models trained on code have shown great potential to increase productivity of software developers. Several execution-based benchmarks have been proposed to evaluate functional correctness of model-generated code on simple…

We present Dependent JavaScript (DJS), a statically-typed dialect of the imperative, object-oriented, dynamic language. DJS supports the particularly challenging features such as run-time type-tests, higher-order functions, extensible…

Modern programming follows the continuous integration (CI) and continuous deployment (CD) approach rather than the traditional waterfall model. Even the development of modern programming languages uses the CI/CD approach to swiftly provide…

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…

Background. Test resources are usually limited and therefore it is often not possible to completely test an application before a release. To cope with the problem of scarce resources, development teams can apply defect prediction to…

Debugging lazy functional programs poses serious challenges. In support of the "stop, examine, and resume" debugging style of imperative languages, some debugging tools abandon lazy evaluation. Other debuggers preserve laziness but present…

Detecting Bug Inducing Commit (BIC) or Just in Time (JIT) defect prediction using Machine Learning (ML) based models requires tabulated feature values extracted from the source code or historical maintenance data of a software system.…