Preparing fine-tuning datasets for large language models (LLMs) commonly involves substantial manual effort, particularly in extracting, structuring, and validating data from unstructured sources. This study proposes a semi-automated, human-in-the-loop approach for generating fine-tuning question–answer (QA) pairs from PDF documents. The research investigates how unstructured textual content can be systematically transformed into validated QA data suitable for fine-tuning, while mitigating the risks associated with hallucinated or low-quality model outputs.The proposed system consists of a web-based architecture combining a React frontend with a Flask backend interfacing with the OpenAI API. Users provide a PDF document and a target page range, after which the system extracts text and generates candidate QA pairs. These candidates are presented for manual inspection, filtering, and refinement, prior to export in a structured JSON format compatible with fine-tuning pipelines.The results indicate that the proposed approach reduces the effort required for manual dataset construction while preserving data quality through mandatory human validation. The study highlights the effectiveness of hybrid automation workflows in accelerating fine-tuning dataset preparation without compromising reliability, and contributes design insights for human-centered tools supporting LLM customization.
Niklas Suvitie, Mika Saari, Pekka Abrahamsson· International Convention on...· 1 citation
Short-term Heart Rate Variability (HRV) forecasting could provide clinicians with actionable lead time for detecting autonomic dysfunction and adverse cardiac events. Consumer wearable devices generate fragmented, artifact-rich HRV signals that challenge conventional forecasting approaches. In this study, we evaluated the forecasting ability of three Time Series Foundation Models (TSFMs), TimesFM, Chronos, and MOIRAI, against traditional baselines (Mean, Exponential Smoothing, and Exponentially Weighted Moving Average) on real-world wearable data collected from 49 healthy individuals. To address data fragmentation, we introduce a variability-preserving imputation method that augments linear interpolation with locally adaptive stochastic noise, retaining physiological dynamics essential for accurate forecasting. The results show that TSFMs outperformed all baselines without fine-tuning, achieving average Mean Absolute Scaled Error (MASE) between 0.81 and 0.87 across TSFMs and both context lengths (32 and 64 time steps), with Chronos and TimesFM as the top models, though MOIRAI showed limited gains over baselines. With up to a 2-hour forecast horizon, the results establish a baseline for TSFMs'performance on a real-world dataset, highlighting domain-specific fine-tuning as a promising direction for clinical deployment.
Luukas Peräkylä, F. Sohrab, Ville Hautamäki et al.· 0 citations
Automated authoring of Gherkin Behavior-Driven Development (BDD) acceptance criteria remains a manual bottleneck in requirements engineering. This study investigates whether epic-organized LLM-generated Gherkin produces higher quality and coverage than requirement-aligned generation. We compare our Timeless (an epic-organized LLM pipeline) approach against a naive large language model (LLM) baseline on four requirements documents (107 requirements) from the PURE dataset. Evaluation covers structural metrics, automated requirement coverage via TF-IDF and dense embeddings, and blind expert assessment by four researchers. In our evaluation, the JSON-constrained pipeline produced structurally valid scenarios across all generated outputs, while the zero-shot baseline achieved 99% structural validity. Semantic coverage was comparable to the baseline, with Timeless achieving 94.3% semantic Requirement Coverage Rate compared with 92.9% for the baseline. TF-IDF produced lower coverage scores for the epic-organized output, suggesting that lexical metrics may miss coverage when scenarios paraphrase requirements at a higher level of abstraction. Expert raters prefer the epic-organized strategy on Correctness (4.61 vs 4.14), Executability (4.61 vs 4.07), and Completeness (4.31 vs 3.50). Overall, the results suggest that epic-organized generation can improve perceived Gherkin quality while maintaining comparable semantic coverage, although broader replication is needed before generalizing this finding.
Shahbaz Siddeeq, M. Abbasi, Jussi Rasku et al.· 0 citations
In agile software development, maintaining high-quality user stories is crucial, but also challenging. This study explores the use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams. We developed a reference model for an Autonomous LLM-based Agent System and implemented it at the company. The quality of user stories in the study and the effectiveness of these agents for user story quality improvement was assessed by 11 participants across six agile teams. Our findings demonstrate the potential of LLMs in improving user story quality, contributing to the research on AI role in agile development, and providing a practical example of the transformative impact of AI in an industry setting.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
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Retrieval-Augmented Generation (RAG) systems are emerging as a key approach for grounding Large Language Models (LLMs) in external knowledge, addressing limitations in factual accuracy and contextual relevance. However, there is a lack of empirical studies that report on the development of RAG-based implementations grounded in real-world use cases, evaluated through general user involvement, and accompanied by systematic documentation of lessons learned. This paper presents five domain-specific RAG applications developed for real-world scenarios across governance, cybersecurity, agriculture, industrial research, and medical diagnostics. Each system incorporates multilingual OCR, semantic retrieval via vector embeddings, and domain-adapted LLMs, deployed through local servers or cloud APIs to meet distinct user needs. A web-based evaluation involving a total of 100 participants assessed the systems across six dimensions: (i) Ease of Use, (ii) Relevance, (iii) Transparency, (iv) Responsiveness, (v) Accuracy, and (vi) Likelihood of Recommendation. Based on user feedback and our development experience, we documented twelve key lessons learned, highlighting technical, operational, and ethical challenges affecting the reliability and usability of RAG systems in practice.
M. Hasan, Muhammad Waseem, Kai-Kristian Kemell et al.· EUROMICRO Conference on Soft...· 10 citations· ⚡1
Refactoring is a constant activity in software development and maintenance. Scale and maintain software systems are based on code refactoring. However, this process is still labor intensive, as it requires programmers to analyze the codebases in detail to avoid introducing new defects. In this research, we put forward a large language model (LLM)-based multi-agent system to automate the refactoring process on Haskell code. The objective of this research is to evaluate the effect of LLM-based agents in performing structured and semantically accurate refactoring on Haskell code. Our proposed multi-agent system based on specialized agents with distinct roles, including code analysis, refactoring execution, verification, and debugging. To test the effectiveness and practical applicability of the multi-agent system, we conducted evaluations using different open-source Haskell codebases. The results of the experiments carried out showed that the proposed LLM-based multi-agent system could average 11.03% decreased complexity in code, an improvement of 22.46% in overall code quality, and increase performance efficiency by an average of 13.27%. Furthermore, memory allocation was optimized by up to 14.57%. These results highlight the ability of LLM-based multi-agent in managing refactoring tasks targeted toward functional programming paradigms. Our findings hint that LLM-based multi-agent systems integration into the refactoring of functional programming languages can enhance maintainability and support automated development workflows.
Shahbaz Siddeeq, Muhammad Waseem, Z. Rasheed et al.· International Conference on...· 4 citations
Anomaly detection in smart power grids is a critical challenge due to the complexity, heterogeneity, and dynamic nature of sensor data streams. Existing one-class classification methods, particularly Subspace Support Vector Data Description (SVDD), have been extended to multimodal scenarios but often fail to fully exploit the structural dependencies across modalities, limiting their robustness in real-world applications. In this paper, we address this gap by proposing a generalized Multimodal Subspace Support Vector Data Description (MS-SVDD) model with graph-embedded regularization. The method projects data from multiple modalities into a shared low-dimensional subspace while preserving modality-specific structure through Laplacian regularizers. Our approach is evaluated on a three-modality dataset derived from smart grid event time series, using a dedicated preprocessing pipeline for constructing one-class classification training samples. The results demonstrate that our graph-embedded MS-SVDD improves robustness of event detection compared to conventional approaches, highlighting the potential of integrating graph priors with multimodal subspace learning for advancing anomaly detection in critical infrastructure. More broadly, this work contributes to the wider field of AI by illustrating how relational and structural information can be systematically embedded into one-class models, enabling robust learning under complex, high-dimensional, and multimodal conditions.
Thomas Debelle, F. Sohrab, Pekka Abrahamsson et al.· Scientific Reports· 1 citation
This paper presents MARARE, a real-time multi-agent system that transforms meeting dialogues into structured software requirements. One agent interacts with participants, while background agents extract and verify requirements collaboratively. Evaluation using the LLM-as-a-Judge method across five meetings (5–8 minutes each) shows a mean coverage of 80.0 ± 11.2 % (mean ± SD), semantic similarity of 0.86 ± 0.05, and hallucination rate of 14.3 ± 6.2 %. Preliminary results indicate performance differences across LLMs, suggesting that model choice influences coverage, consistency, and hallucination rates.
Malik Abdul Sami, Gessé Evangelista, Kai-Kristian Kemell et al.· AGENT@ICSE· 0 citations
Context: Organizations adopting Artificial Intelligence (AI) face challenges in eliciting and analyzing requirements that align with strategic objectives, especially when human oversight and iterative refinement are needed. Large Language Models (LLMs)-based Multi-agent systems provide a potential solution by supporting structured and collaborative Requirements Engineering (RE) processes for AI adoption planning.
Objective: The objective of this study is to investigate whether a multi-agent system, built on LLMs and supported by human input, can assist in requirements analysis for AI adoption. Method: We used a mixed-method approach: (i) designed and developed a multi-agent system to support the generation and prioritization of requirements for AI adoption, (ii) conducted multiple case studies with four companies to evaluate the system, and (iii) collected data through post-session questionnaires from nine participants and follow-up interviews, one per company.
Results: Questionnaire and interview findings together indicate that the system may assist in identifying relevant and goal-aligned requirements. Seven participants considered the generated requirements relevant, and six found them aligned with organizational goals. Participants noted that iterative feedback improved completeness and feasibility, often within two feedback rounds. Both data sources show that human input was essential to clarify technical details, ensure contextual accuracy, and validate prioritization results. Participants from all companies also identified usability, transparency, and scalability as areas requiring further refinement for broader organizational use.
Conclusions: LLM-based multi-agent systems can support strategic AI planning by enabling iterative refinement with human experts. Future work will include more interviews with stakeholders and adjustments to system features to improve transparency, usability, and scalability.
Malik Abdul Sami, Zheying Zhang, Muhammad Waseem et al.· e-Informatica Software Engin...· 6 citations
Large Language Models (LLMs) offer new opportunities for automated code refactoring. However, generated changes must reduce targeted quality problems without introducing new issues or altering behaviour-relevant code structures. We introduce REFINE (Refactoring with Evidence-aware Flow for Integrated ageNtic Execution), a tool-agnostic, evidence-aware multi-agent approach for generating Java file-level refactoring candidates. REFINE combines static-analysis-guided smell identification, smell-informed planning, LLM-based transformation, automated re-analysis, preservation checks, and structured reporting. We evaluate REFINE on 450 Java files from 15 open-source systems, producing 1,350 model-pass outputs using OpenAI GPT-5.5, Google Gemini 3.1 Pro Preview, and Anthropic Claude Opus 4.8. REFINE reduces detected code smells by 68.26%, 72.79%, and 68.49% across the three configurations, respectively, with the strongest reductions observed for major smells. A matched 150-file direct-prompt baseline shows that REFINE achieves a higher median code-smell reduction with smaller edits and fewer public-method removals. However, broader quality improvements are inconsistent, and preservation checks reveal residual risks, including assert/fail-call changes and public-method removal. Therefore, REFINE outputs should be treated as refactoring candidates requiring compilation, testing, dependency analysis, and human review before adoption in repository- or system-level settings.
Muhammad Waseem, Aakash Ahmad, Pekka Abrahamsson· 0 citations
This paper investigates how multi-agent systems (MAS-based on large language models) can support actuarial risk modelling, with a particular focus on uncertainty quantification, and proposes a novel approach to uncertainty propagation using token-level log-probabilities and a Bayesian Network.
Bart Custers, K. Aslansefat· Lecture notes in computer sc...· 0 citations
Part IV of a series on equity and reliability in medical imaging AI. Part III of this series showed that naming a demographic group in the text prompt of a medical vision–language model changes its diagnosis on a fixed chest radiograph, that the perturbation is exactly rank one in the standard positive/negative pair readout, and that group-specific thresholds therefore provably cannot remove it. Two questions were left open: whether any of this survives a change of imaging domain, and whether it matters for a model that is actually good at its task — Part III's strongest encoder reached a mean AUC of 0.687. This paper audits four pathology vision–language models (PLIP, QuiltNet-B-32, CONCH, and the slide-level TITAN) under the descriptor set from Part III, unchanged, and combines them with Part III's three radiology encoders for a seven-model comparison spanning two imaging domains and both patch and slide granularity. On TITAN the audit runs at full strength: 8,010 whole-slide images from 6,582 patients across 25 TCGA cancer types, every interval clustered on patient, mean neutral-prompt AUC 0.982 with all 25 projects clearing the utility floor. Four results. The rank-one identity transfers exactly — verified to 5.2×10−8 on real slide embeddings, two orders of magnitude tighter than Part III because TITAN has no learned logit scale to amplify float error. Part III's threshold-irremovability corollary therefore holds for whole-slide pathology. Diagnostic competence does not protect against prompt-channel bias. Across seven encoders, neutral-prompt AUC does not predict the effect (r = −0.443, p = 0.32): CONCH at AUC 0.849 shows 21.1 points of mean absolute excess false-negative rate, indistinguishable from OpenAI CLIP at AUC 0.518. What predicts it is a purely geometric quantity, the descriptor perturbation measured against the diagnostic contrast (r = +0.780, p = 0.039), reported as a hypothesis with n = 7 rather than a law. The model answers the token and not its truth, including for race. Part III could test congruence only for sex and age, because NIH ChestX-ray14 records no race; TCGA does, and the difference-in-differences estimator gives a median 0.0045 logits for race and 0.0010 for sex, against a specimen-site positive control 164–730× larger in 4 of 4 pairs at tighter precision. Anatomically impossible descriptors — "a male patient showing endometrial carcinoma" — move the score as much as coherent ones. Prompt symmetrisation is a domain-dependent intervention rather than a general one: it removes 59–63% of the effect on radiology encoders, 26–34% on pathology patch encoders, and amplifies the effect by 94% on TITAN. The text-side symmetry residual separates the two domains completely (Mann–Whitney p = 0.029). The practical consequence is a correction to how this series has framed mitigation. A deployer cannot infer prompt-channel robustness from a model's accuracy, and cannot assume a mitigation validated in one imaging domain transfers to another — including on the strength of Part III. Honest scope. Unlike Parts II, IIb, and III, this part is not preregistered; PROTOCOL_PART4.md is retrospective and says so in its first line, recording which hypotheses preceded their endpoints and which did not. Only TITAN carries interval estimates: the public CRC-VAL-HE-7K mirror records no patient identifiers, so the three patch-level encoders are reported as point estimates only. The anatomically-impossible result rests on five cells and is a demonstration of mechanism, not an estimate of magnitude. This paper also supplies the out-of-domain evidence behind amendment 8 to Part III's protocol, which withdrew Part III's proposed text-only predictor of symmetrisation failure. Files. The manuscript PDF (21 pp, 4 figures); the retrospective protocol; and an artifact archive containing all 13 analysis scripts and every result table as CSV/JSON. Model weights are not redistributed — TITAN, CONCH, and UNI are CC-BY-NC-ND-4.0 and were obtained through the gated Hugging Face process. Ethics. No patient data was collected. TCGA is a public consortium dataset; CRC-VAL-HE-7K is public and de-identified. This is an audit of a model property, not a clinical study, and makes no claim about patient outcomes.
Omar Mohammed· Zenodo (CERN European Organi...· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
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