{"ID":5443874,"CreatedAt":"2026-07-01T02:07:11.383974684Z","UpdatedAt":"2026-07-03T17:12:03.69683831Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31971","arxiv_id":"2606.31971","title":"CoCoMUT: A Tool for Code-Context Mining and Automated Dataset Generation","abstract":"Software-engineering assistants often need method-level context beyond an isolated body, including enclosing-class information, documentation, callers, callees, type hierarchy, and structural characteristics. Manually collecting this context is time-consuming, inconsistent, and difficult to reproduce across large Java projects. We present CoCoMUT, a Java tool for Code-Context Mining and Automated Dataset Generation. CoCoMUT extracts context for a focal method or generates datasets at class, package, or system scope. It discovers project structure, resolves build and classpath information, constructs a SootUp static call graph, and reconciles bytecode-level call edges with Spoon-based source extraction. Each method record combines source, class, documentation, call-graph, and metadata context, providing reproducible inputs for training and running learned software-engineering techniques. The key contribution is a reusable, task-independent pipeline that unifies build discovery, source extraction, call-graph construction, source-bytecode reconciliation, and versioned JSON dataset generation. The resulting records can be consumed individually as context for a focal method or collectively as datasets for documentation, explanation, testing, review, repair, search, and program-comprehension workflows. We evaluate CoCoMUT on 20 real-world Java repositories evenly split between Maven and Gradle. CoCoMUT processed all 20 repositories, emitting 56,512 method-context records and 386,048 serialized call edges. Among call edges whose bytecode targets belonged to project source, CoCoMUT reconciled 97.8% to source method identities. In a manual audit of 200 randomly sampled methods across 10 systems, 99.0% of generated context records passed all applicable correctness checks.","short_abstract":"Software-engineering assistants often need method-level context beyond an isolated body, including enclosing-class information, documentation, callers, callees, type hierarchy, and structural characteristics. Manually collecting this context is time-consuming, inconsistent, and difficult to reproduce across large Java...","url_abs":"https://arxiv.org/abs/2606.31971","url_pdf":"https://arxiv.org/pdf/2606.31971v1","authors":"[\"Alessandro Botta\",\"Shiven Garisa\",\"Jaya Vardhini Akurathi\",\"Ahsanul Ameen Sabit\",\"Trey Woodlief\",\"Soneya Binta Hossain\"]","published":"2026-06-30T17:12:44Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[]","has_code":false}