Conditional branching provides a fundamental structure for executing a specific branch based on the value of a boolean expression at run-time. However, repeated or nested conditional branching can lead to increased complexity. Furthermore, fall-through semantics in conditional branching result in uncontrollable jumps. Refactoring conditional branching manually is error-prone, time-consuming, and tedious. There is a critical need to provide automated refactoring support for conditional branching. To this end, this paper presentsReBrancher, an automated refactoring approach to eliminate repeated or nested conditional branching. Firstly,ReBrancherparses source code into an abstract syntax tree and walks through conditional branching statements. Secondly, it removes redundant fall-through semantics by static program analysis and an automaton. The automaton is constructed from a control flow graph to match patterns of conditional branching. Finally, it converts a conditional branching into aswitchexpression and removes the fall-through semantics.ReBrancherwas evaluated on nine real-world projects involving 25,137 conditional branchings. Experimental results show that a total of 1,790 conditional branching constructs are refactored within an average of 18.62 seconds per project. Furthermore,ReBrancherreduced the average cyclomatic complexity by 4.41% and removed 1,249 code smells, demonstrating its effectiveness in improving code quality.

Software refactoring is widely employed to improve software quality. However, conducting refactorings manually is tedious, time-consuming, and error-prone. Consequently, automated and semi-automated tool support is highly desirable for software refactoring in the industry, and most of the main-stream IDEs provide powerful tool support for refactoring. However, complex refactoring engines are prone to errors, which in turn may result in imperfect and incorrect refactorings. To this end, in this paper, we propose a ChatGPT-based approach to testing refactoring engines. We first manually analyze bug reports and test cases associated with refactoring engines, and construct a feature library containing fine-grained features that may trigger defects in refactoring engines. The approach automatically generates prompts according to both predefined prompt templates and features randomly selected from the feature library, requesting ChatGPT to generate test programs with the requested features. Test programs generated by ChatGPT are then forwarded to multiple refactoring engines for differential testing. To the best of our knowledge, it is the first approach in testing refactoring engines that guides test program generation with features derived from existing bugs. It is also the first approach in this line that exploits LLMs in the generation of test programs. Our initial evaluation of four main-stream refactoring engines suggests that the proposed approach is effective. It identified a total of 115 previously unknown bugs besides 28 inconsistent refactoring behaviors among different engines. Among the 115 bugs, 78 have been manually confirmed by the original developers of the tested engines, i.e., IntelliJ IDEA, Eclipse, VScode-Java, and NetBeans.

Copy-paste-modify is a widespread and pragmatic practice in software development, where developers adapt reused code snippets, sourced from platforms such as Stack Overflow, GitHub, or LLM outputs, into their local codebase. A critical yet underexplored aspect of this adaptation is code wiring: the context-aware process of substituting unresolved variables in pasted code with suitable variables or expressions from the surrounding context. Existing solutions either rely on heuristic rules or historical templates, often failing to effectively utilize contextual information, despite studies showing that over half of adaptation cases are context-dependent. In this paper, we introduce WIRL, an LLM-based agent for code wiring framed as a Retrieval-Augmented Generation (RAG) infilling task. WIRL combines an LLM, a customized toolkit, and an orchestration module to identify unresolved variables, retrieve context, and perform context-aware substitutions. To balance efficiency and autonomy, the agent adopts a mixed strategy: deterministic rule-based steps for common patterns, and a state-machine-guided decision process for intelligent exploration. We evaluate WIRL on a carefully curated, high-quality dataset consisting of real-world code adaptation scenarios. Our approach achieves an exact match precision of 91.7% and a recall of 90.0%, outperforming advanced LLMs by 22.6 and 13.7 percentage points in precision and recall, respectively, and surpassing IntelliJ IDEA by 54.3 and 49.9 percentage points. These results underscore its practical utility, particularly in contexts with complex variable dependencies or multiple unresolved variables. We believe WIRL paves the way for more intelligent and context-aware developer assistance in modern IDEs.

Renaming is one of the most popular software refactorings. Although developers may know what the new name should be when they conduct a renaming, it remains valuable for refactoring tools to recommend new names automatically so that developers can simply hit Enter and efficiently accept the recommendation to accomplish the refactoring. Consequently, most IDEs automatically recommend new names for renaming refactorings by default. However, the recommendation made by mainstream IDEs is often incorrect. For example, the precision of IntelliJ IDEA in recommending names for field renamings is as low as 6.3%. To improve the accuracy, in this paper, we propose a context-aware lightweight approach (called CARER) to recommend new names for Java field renamings. Different from mainstream IDEs that rely heavily on initializers and data types of the to-be-renamed fields, CARER exploits both dynamic and static contexts of the renamings as well as naming conventions. We evaluate CARER on 1.1K real-world field renamings discovered from open-source applications. Our evaluation results suggest that CARER can significantly improve the state of the practice in recommending new names for field renamings, improving the precision from 6.30% to 61.15%, and recall from 6.30% to 41.50%. Our evaluation results also suggest that CARER is as efficient as IntelliJ IDEA is, making it suitable to be integrated into IDEs.

Abstract Code smell is the structural design defect that makes programs difficult to understand, maintain, and evolve. Existing works of code smell detection mainly focus on prevalent code smells, such as feature envy, god class, and long method. Few works have been done on detecting brain class/method. Furthermore, existing deep‐learning‐based approaches leverage the CNN model to improve accuracy by barely increasing the number of layers, which may cause a problem of gradient degradation. To this end, this paper proposes a novel approach called MARS to detect brain class/method. MARS improves the gradient degradation by employing an improved residual network. It increases the weight value of those important code metrics to label smelly samples by introducing a metric–attention mechanism. To support the training of MARS, a dataset called BrainCode is generated by extracting more than 270,000 samples from 20 real‐world applications. MARS is evaluated on BrainCode and compared to other machine‐learning‐based and deep‐learning‐based approaches. The experimental results demonstrate that the average accuracy of MARS is 2.01 % higher than that of the existing approaches, which improves state‐of‐the‐art.