Bug reproduction is a critical developer activity that is also challenging to automate, as bug reports are often in natural language and thus can be difficult to transform to test cases consistently. As a result, existing techniques mostly focused on crash bugs, which are easier to automatically detect and verify. In this work, we overcome this limitation by using large language models (LLMs), which

Software developers spend a significant portion of time fixing bugs in their projects. To streamline this process, bug localization approaches have been proposed to identify the source code files that are likely responsible for a particular bug. Prior work proposed several similarity-based machine-learning techniques for bug localization. Despite significant advances in these techniques, they do not

As software continues to evolve and business functions become increasingly complex, architectural inconsistency arises when the implementation architecture deviates from the expected architecture design. This architectural problem makes maintenance difficult and requires significant effort to refactor. To assist labor-intensive refactoring, automated refactoring has received much attention such as

Test-to-code traceability links (TCTLs) establish links between test artifacts and code artifacts. These links enable developers and testers to quickly identify the specific pieces of code tested by particular test cases, thus facilitating more efficient debugging, regression testing, and maintenance activities. Various approaches, based on distinct concepts, have been proposed to establish method-level

Recent neural models of code, such as OpenAI Codex and AlphaCode, have demonstrated remarkable proficiency at code generation due to the underlying attention mechanism. However, it often remains unclear how the models actually process code, and to what extent their reasoning and the way their attention mechanism scans the code matches the patterns of developers. A poor understanding of the model reasoning