CoCoMUT: A Tool for Code-Context Mining and Automated Dataset Generation

2026-06-30

AI Summary

This work addresses the high cost, inconsistency, and poor reproducibility associated with manual collection of method-level contextual information—such as class metadata, documentation, and call relationships—in large-scale Java projects. To overcome these challenges, the authors propose the first task-agnostic, reusable unified pipeline that automatically parses Maven/Gradle project structures and classpaths, leverages SootUp to construct static call graphs, employs Spoon for source code analysis, and achieves precise alignment between source code and bytecode to generate a versioned, multidimensional context dataset. Evaluated on 20 real-world repositories, the pipeline successfully processes 56,512 methods and 386,048 call edges, with 97.8% of intra-project call edges accurately mapped to source code locations and a human-audited correctness rate of 99.0%.

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.

Problem

• code context
• method-level context
• automated dataset generation
• software engineering
• Java projects

Innovation

• code-context mining
• automated dataset generation
• static call graph
• source-bytecode reconciliation
• software engineering assistants