Framework-based multilingual software development (MLSD) is becoming prevalent in software engineering practice. Despite the advantages, framework-based MLSD also leads to reduced understandability and changeability of multilingual software, due to the introduced cross-language links (XLLs). To help alleviate this problem, there are existing practice and research crafting rules to specify and detect XLLs, but only focusing on specific frameworks. With the intention of coping with the diversity of XLL conventions across different multi-lingual frameworks, this paper proposes Focused, an extensible approach to framework-oriented cross-language link specification and detection. The basic idea is to decouple the two activities of XLL specification and detection as much as possible by mediating between them with a set of DSL-enabled XLL rules, making Focused configurable to different multilingual frameworks. We evaluated Focused on 3 widely-used multilingual frameworks and 15 high-starred open-source projects using these frameworks, showing the expressiveness, effectiveness, and efficiency of Focused.

The performance of repository-level code completion depends upon the effective leverage of both general and repository-specific knowledge. Despite the impressive capability of code LLMs in general code completion tasks, they often exhibit less satisfactory performance on repository-level completion due to the lack of repository-specific knowledge in these LLMs. To address this problem, we propose GraphCoder, a retrieval-augmented code completion framework that leverages LLMs' general code knowledge and the repository-specific knowledge via a graph-based retrieval-generation process. In particular, GraphCoder captures the context of completion target more accurately through code context graph (CCG) that consists of control-flow, data- and control-dependence between code statements, a more structured way to capture the completion target context than the sequence-based context used in existing retrieval-augmented approaches; based on CCG, GraphCoder further employs a coarse-to-fine retrieval process to locate context-similar code snippets with the completion target from the current repository. Experimental results demonstrate both the effectiveness and efficiency of GraphCoder: Compared to baseline retrieval-augmented methods, GraphCoder achieves higher exact match (EM) on average, with increases of +6.06 in code match and +6.23 in identifier match, while using less time and space.

Framework-based multi-lingual software is increasingly prevalent, but it also brings negative effects and extra burden on software maintenance and evolution, because of the introduced cross-language code coupling, which are usually mixed with framework-specific conventions. Researchers have proposed various approaches to code coupling detection, but there is still a lack of necessary support for cross-language coupling detection in framework-based software development. In this paper, we present a preliminary study about cross-language coupling detection in software development based on the Android application framework. We investigate the characteristics of multi-lingual changes in the top-100 starred open-source Android repositories on GitHub, and find that multi-lingual commits are non-trivial: their code changes are more scattered, and more inclined to introduce bugs than other commits. To mitigate the side-effect of multi-lingual development, we propose Grace, a Graph-based cross-language co-change suggestion approach for Android application development. Grace (a) designs a language-agnostic graph to represent code elements from different languages, and (b) employs an entity-based collaborative filtering algorithm to detect and rank candidates of cross-language code couplings, from the graph representation of the latest version as well as the historical multi-lingual commits of a repository. To evaluate the effectiveness of Grace, we apply it to the two tasks of cross-language co-change suggestion and inconsistency checking. Results show that Grace (a) can effectively suggest cross-language co-changed files and types, and (b) can also find existing and potential bugs or code smells caused by inconsistent co-changes.

Code merging plays an important role in collaborative software development. However, it is often tedious and error-prone for developers to manually resolve merge conflicts, especially when there are many conflicts after merging long-lived branches or parallel versions. In this paper, we present SoManyConflicts, a language-agnostic approach to help developers resolve merge conflicts systematically, by utilizing their interrelations (e.g., dependency, similarity, etc.). SoManyConflicts employs a graph representation to model these interrelations and provides 3 major features: 1) cluster and order related conflict based on the graph connectivity; 2) suggest related conflicts of one focused conflict based on the topological sorting, 3) suggest resolution strategies for unresolved conflicts based already resolved ones. We have implemented SoManyConflicts as a Visual Studio Code extension that supports multiple languages (Java, JavaScript, and TypeScript, etc.), which is briefly introduced in the video: https://youtu.be/asWhj1KTU. The source code is publicly available at: https://github.com/Symbolk/somanyconflicts.