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From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora Personalized Benchmarking: Evaluating LLMs by Individual Preferences Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations JFinTEB: Japanese Financial Text Embedding Benchmark UsefulBench: Towards Decision-Useful Information as a Target for Information Retrieval SIMMER: Cross-Modal Food Image--Recipe Retrieval via MLLM-Based Embedding Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts Collaborative Filtering Through Weighted Similarities of User and Item Embeddings IG-Search: Step-Level Information Gain Rewards for Search-Augmented Reasoning Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model Evaluation of Agents under Simulated AI Marketplace Dynamics
Incremental Analysis of Legacy Applications Using Knowled...
Saravanan Krishnan, Amith Singhee, Keerthi Narayan Raghunath, Al · 2025-05-11 · via cs.IR updates on arXiv.org

Industries such as banking, telecom, and airlines - o6en have large so6ware systems that are several decades old. Many of these systems are written in old programming languages such as COBOL, PL/1, Assembler, etc. In many cases, the documentation is not updated, and those who developed/designed these systems are no longer around. Understanding these systems for either modernization or even regular maintenance has been a challenge. An extensive application may have natural boundaries based on its code dependencies and architecture. There are also other logical boundaries in an enterprise setting driven by business functions, data domains, etc. Due to these complications, the system architects generally plan their modernization across these logical boundaries in parts, thereby adopting an incremental approach for the modernization journey of the entire system. In this work, we present a so6ware system analysis tool that allows a subject ma=er expert (SME) or system architect to analyze a large so6ware system incrementally. We analyze the source code and other artifacts (such as data schema) to create a knowledge graph using a customizable ontology/schema. Entities and relations in our ontology can be defined for any combination of programming languages and platforms. Using this knowledge graph, the analyst can then define logical boundaries around dependent Entities (e.g. Programs, Transactions, Database Tables etc.). Our tool then presents different views showcasing the dependencies from the newly defined boundary to/from the other logical groups of the system. This exercise is repeated interactively to 1) Identify the Entities and groupings of interest for a modernization task and 2) Understand how a change in one part of the system may affect the other parts. To validate the efficacy of our tool, we provide an initial study of our system on two client applications.