Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2011.07313

Classification of Reverse-Engineered Class Diagram and Forward-Engineered Class Diagram using Machine Learning

Kaushil Mangaroliya·Het Patel
View original source ↗Read source document ↗Export metadata ↓

Abstract

UML Class diagram is very important to visualize the whole software we are working on and helps understand the whole system in the easiest way possible by showing the system classes, its attributes, methods, and relations with other objects. In the real world, there are two types of Class diagram engineers work with namely 1) Forward Engineered Class Diagram (FwCD) which are hand-made as part of the forward-looking development process, and 2). Reverse Engineered Class Diagram (RECD) which are those diagrams that are reverse engineered from the source code. In the software industry while working with new open software projects it is important to know which type of class diagram it is. Which UML diagram was used in a particular project is an important factor to be known? To solve this problem, we propose to build a classifier that can classify a UML diagram into FwCD or RECD. We propose to solve this problem by using a supervised Machine Learning technique. The approach in this involves analyzing the features that are useful in classifying class diagrams. Different Machine Learning models are used in this process and the Random Forest algorithm has proved to be the best out of all. Performance testing was done on 999 Class diagrams.

Explore related subjects

cs.SEcs.LG

Keep this discovery

BibTeXRIS

Kaushil Mangaroliya, Het Patel. 2020-11-14. Classification of Reverse-Engineered Class Diagram and Forward-Engineered Class Diagram using Machine Learning. https://arxiv.org/abs/2011.07313

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

LLMVul: A Vulnerability-Labeled Dataset of LLM-Generated C/C++ Functions from Real Production Repositories

Large language models (LLMs) are increasingly used to generate and assist with software development, yet existing vulnerability datasets largely focus on human-written code or controlled prompting environments. This limits the ability to study security weaknesses in LLM-generated code as it appears in real-world software projects. We present LLMVul, a vulnerability-labeled dataset of LLM-generated C/C++ functions mined from real production repositories. We mine AI-assisted development activity from GitHub over a 4 year period, from November 13, 2022 to September 3, 2026, using provenance signals such as commit metadata and AI-related authorship evidence. After filtering and deduplication, LLMVul contains 21,430 unique C/C++ functions from 226 repositories, together with repository, commit, function, provenance, and AI-tool metadata. We establish vulnerability labels using an ensemble of complementary static-analysis and pattern-based techniques and assign Common Weakness Enumeration (CWE) categories to confirmed vulnerable functions. To assess labeling reliability, we additionally conduct independent manual annotation and measure inter-rater agreement using Cohen's kappa ($k=0.79$). LLMVul contains 1,540 ensemble-vulnerable functions spanning 17 unique CWE categories, providing substantially more real-world LLM-generated vulnerable C/C++ functions than existing vulnerability-oriented LLM code benchmarks. By preserving both code-level vulnerability labels and generation/provenance metadata, LLMVul enables reproducible research on vulnerability detection, security evaluation of LLM-generated code, and analysis of vulnerability patterns in AI-assisted software development. The LLMVul dataset is publicly available at https://doi.org/10.5281/zenodo.22668216.

cs.SE↗
arXiv2026

What a Random Draw from the MCP Registry Contains, and What Tool-Use Benchmarks Contain Instead

Studies of the Model Context Protocol (MCP) server ecosystem draw their samples in ways that quietly select for servers that work: reference sets, popularity lists, hand-curated frames, or pipelines that repair a server until it starts. We report what an unrepaired probability sample actually contains. From a 24,135-server registry census we draw 400 npm/stdio servers with a published seed and probe each one over the wire. Only 48.8% complete an initialize handshake, against 66.7% for a hand-curated frame measured with the same instrument, and the dominant failure is not missing credentials (13.3%) but servers that never start at all (37.5%). Among the 195 that do run, hard conformance is total: zero fatal JSON Schema violations across 2,766 advertised tools. Optional safety annotations are the real variance, and the tool-level omission rate on a random draw is 58.8% against 41.5% on the curated frame, so curation flatters this figure too. We then compare the tool descriptions these servers advertise against two tool-use benchmark corpora using one method held constant. Real MCP tools show 2.8% near-duplication at cosine 0.70, and all of it lies within single servers: cross-author near-duplication is 0.0% at every threshold tested. BFCL v4 shows 16.7%, of which 16.4 points lie between independently presented tasks. UltraTool shows 0.3%, cleaner than real tools, so this is a property of BFCL and not of synthetic corpora as a class. Separately, 68.8% of raw BFCL rows and 85.6% of raw UltraTool rows are exact name-plus-description repeats, against 0.4% for real MCP, so any statistic computed over these releases without global deduplication measures repetition rather than tools. All figures regenerate from released scripts and a published seed.

cs.SE↗
arXiv2026

Engineering Reliable Commit Gates for Agentic AI: Cost-Aware Verification Portfolios under Common-Mode Data Failures

Agentic systems commit state-changing actions, but additional verifiers can inherit the same upstream fault. We present VP-CONTROL, a runtime-assurance design and deterministic benchmark for cost-aware commit gates. Its 48 task templates yield 2,880 scenarios across six fault regimes. A fixed-call 2 x 2 experiment separates verifier-model diversity from evidence-source diversity. On frozen proposals from two local actor families, a cross-model vote over shared evidence approves 62.9% of unsafe proposals, versus 22.9% with an independent source. The source effect is 40.9 percentage points, compared with 11.3 for model diversity. A portfolio controller selects verification plans using only deployment-observable metadata. Approximate cluster-adjusted calibration at a nominal 5% per-task target yields 1.9% unsafe execution and 38.2% automated safe coverage on the locked test. Matched-budget portfolios also improve on fixed verification policies. Transfer remains conditional: unseen fault families yield 16-26% risk, and a FinQA check fails to reproduce the source effect with the tested small verifiers. A preregistered live HTTP/SQLite study tests concurrent writes and lost responses. After-check races defeat verifier-only gates; transactional partial guards prevent only covered failures, while a full atomic guard records no unsafe effects across 216 episodes. Idempotent request identifiers prevent duplicate effects after lost responses. The results motivate explicit evidence lineage, cost-aware selection, and commit-time enforcement, while exposing the limits of approximate calibration and local-tool generalization.

cs.SE↗