arXiv subjects

Mark Cole

Publications and source records attributed to Mark Cole.

3 recordsLinked to original sources

arXiv2026

Question Answering for Diagram-Rich Technical Meeting Videos

Software engineering increasingly relies on asynchronous communication artifacts, including recorded meetings where stakeholders discuss concerns, rationale, and decisions. These meetings often include diagram-based representations of requirements, system behavior, component interactions, and trace dependencies. Accessing knowledge from these meetings is challenging because recordings are long and relevant evidence is distributed across speech, slides, and technical diagrams. This paper reports our industrial experience developing and evaluating LMVQA, an LLM-based multimodal question-answering system for technical meeting videos. Developed in collaboration with engineers at Ciena, LMVQA supports the understanding of requirements and design intent by grounding answers in audio and visual evidence, with explicit handling of diagram-rich content such as requirements and UML diagrams. It processes each video once to build a reusable time-stamped evidence corpus for grounded question answering. Across a Ciena dataset and a public dataset, we show that LMVQA significantly improves answer accuracy compared to a state-of-the-art baseline, from 31% to 94% on the Ciena dataset and from 21% to 88% on the public dataset, with larger gains on diagram-rich videos. We further show that, after one-time indexing, LMVQA reduces average response time from 81.3s to 3.3s on Ciena and from 98.4s to 9.2s on the public dataset, while lowering average token-based LLM API cost by about 75%. Finally, our interviews with three domain experts show that engineers particularly value LMVQA for locating software-engineering-relevant information, revisiting rationale, and tracing answers to specific video segments.

cs.SE↗

arXiv2026

Question Answering for Multi-Release Systems: A Case Study at Ciena

Companies regularly have to contend with multi-release systems, where several versions of the same software are in operation simultaneously. Question answering over documents from multi-release systems poses challenges because different releases have distinct yet overlapping documentation. Motivated by the observed inaccuracy of state-of-the-art question-answering techniques on multi-release system documents, we propose QAMR, a chatbot designed to answer questions across multi-release system documentation. QAMR enhances traditional retrieval-augmented generation (RAG) to ensure accuracy in the face of highly similar yet distinct documentation for different releases. It achieves this through a novel combination of pre-processing, query rewriting, and context selection. In addition, QAMR employs a dual-chunking strategy to enable separately tuned chunk sizes for retrieval and answer generation, improving overall question-answering accuracy. We evaluate QAMR using a public software-engineering benchmark as well as a collection of real-world, multi-release system documents from our industry partner, Ciena. Our evaluation yields five main findings: (1) QAMR outperforms a baseline RAG-based chatbot, achieving an average answer correctness of 88.5% and an average retrieval accuracy of 90%, which correspond to improvements of 16.5% and 12%, respectively. (2) An ablation study shows that QAMR's mechanisms for handling multi-release documents directly improve answer accuracy. (3) Compared to its component-ablated variants, QAMR achieves a 19.6% average gain in answer correctness and a 14.0% average gain in retrieval accuracy over the best ablation. (4) QAMR reduces response time by 8% on average relative to the baseline. (5) The automatically computed accuracy metrics used in our evaluation strongly correlate with expert human assessments, validating the reliability of our methodology.

cs.SE↗

arXiv2025

The Impact of Critique on LLM-Based Model Generation from Natural Language: The Case of Activity Diagrams

Large Language Models (LLMs) show strong potential for automating model generation from natural-language descriptions. A common approach begins with an initial model generation, followed by an iterative critique-refine loop in which the model is evaluated for issues and refined based on those issues. This process needs to address: (1) structural correctness -- compliance with well-formedness rules -- and (2) semantic alignment -- accurate reflection of the intended meaning in the source text. We present LADEX (LLM-based Activity Diagram Extractor), a pipeline for deriving activity diagrams from natural-language process descriptions using an LLM-driven critique-refine process. Structural checks in LADEX can be performed either algorithmically or by an LLM, while alignment checks are performed by an LLM. We design five ablated variants of LADEX to study: (i) the impact of the critique-refine loop itself, (ii) the role of LLM-based semantic checks, and (iii) the comparative effectiveness of algorithmic versus LLM-based structural checks. To evaluate LADEX, we compare generated diagrams with expert ground truths using a trace-based behavioural and an LLM-based matcher. This enables automated measurement of correctness (whether the generated activity diagram includes the ground-truth nodes) and completeness (how many of the ground-truth nodes the generated activity diagram covers). Experiments on two datasets -- a public-domain dataset and an industry dataset from our collaborator, Ciena -- indicate: (1) Both matchers yield similar completeness and correctness comparisons. (2) The critique-refine loop improves structural validity, correctness, and completeness compared to single-pass generation. (3) Activity diagrams refined based on algorithmic structural checks achieve structural consistency, whereas those refined based on LLM-based checks often still show structural inconsistencies.

cs.SE↗