Jameleddine Hassine

Nouf Alturayeif, Irfan Ahmad, Jameleddine Hassine

Requirements traceability, the process of establishing and maintaining relationships between requirements and various software development artifacts, is paramount for ensuring system integrity and fulfilling requirements throughout the Software Development Life Cycle (SDLC). Traditional methods, including manual and information retrieval models, are labor-intensive, error-prone, and limited by low precision. Recently, Large Language Models (LLMs) have demonstrated potential for supporting software engineering tasks through advanced language comprehension. However, a substantial gap exists in the systematic design and evaluation of prompts tailored to extract accurate trace links. This paper introduces TraceLLM, a systematic framework for enhancing requirements traceability through prompt engineering and demonstration selection. Our approach incorporates rigorous dataset splitting, iterative prompt refinement, enrichment with contextual roles and domain knowledge, and evaluation across zero- and few-shot settings. We assess prompt generalization and robustness using eight state-of-the-art LLMs on four benchmark datasets representing diverse domains (aerospace, healthcare) and artifact types (requirements, design elements, test cases, regulations). TraceLLM achieves state-of-the-art F2 scores, outperforming traditional IR baselines, fine-tuned models, and prior LLM-based methods. We also explore the impact of demonstration selection strategies, identifying label-aware, diversity-based sampling as particularly effective. Overall, our findings highlight that traceability performance depends not only on model capacity but also critically on the quality of prompt engineering. In addition, the achieved performance suggests that TraceLLM can support semi-automated traceability workflows in which candidate links are reviewed and validated by human analysts.

Ali Arabat, Mohammed Sayagh, Jameleddine Hassine

As software systems grow in complexity, accurately identifying and managing dependencies among changes becomes increasingly critical. For instance, a change that leverages a function must depend on the change that introduces it. Establishing such dependencies allows CI/CD pipelines to build and orchestrate changes effectively, preventing build failures and incomplete feature deployments. In modern software systems, dependencies often span multiple components across teams, creating challenges for development and deployment. They serve various purposes, from enabling new features to managing configurations, and can even involve traditionally independent changes like documentation updates. To address these challenges, we conducted a preliminary study on dependency management in OpenStack, a large-scale software system. Our study revealed that a substantial portion of software changes in OpenStack over the past 10 years are interdependent. Surprisingly, 51.08% of these dependencies are identified during the code review phase-after a median delay of 5.06 hours-rather than at the time of change creation. Developers often spend a median of 57.12 hours identifying dependencies, searching among a median of 463 other changes. To help developers proactively identify dependencies, we propose a semi-automated approach that leverages two ML models. The first model predicts the likelihood of dependencies among changes, while the second identifies the exact pairs of dependent changes. Our proposed models demonstrate strong performance, achieving average AUC scores of 79.33% and 91.89%, and Brier scores of 0.11 and 0.014, respectively. Indeed, the second model has a good top-k recall across all types of pairs, while the top-k precision has room for improvement.

Asma Yamani, Nadeen AlAmoudi, Salma Albilali et al.

Machine Learning (ML) has been integrated into various software and systems. Two main components are essential for training an ML model: the training data and the ML algorithm. Given the critical role of data in ML system development, it has become increasingly important to assess the quality of data attributes and ensure that the data meets specific requirements before its utilization. This work proposes an approach to guide non-experts in identifying data requirements for ML systems using goal modeling. In this approach, we first develop the Data Requirement Goal Model (DRGM) by surveying the white literature to identify and categorize the issues and challenges faced by data scientists and requirement engineers working on ML-related projects. An initial DRGM was built to accommodate common tasks that would generalize across projects. Then, based on insights from both white and gray literature, a customization mechanism is built to help adjust the tasks, KPIs, and goals' importance of different elements within the DRGM. The generated model can aid its users in evaluating different datasets using GRL evaluation strategies. We then validate the approach through two illustrative examples based on real-world projects. The results from the illustrative examples demonstrate that the data requirements identified by the proposed approach align with the requirements of real-world projects, demonstrating the practicality and effectiveness of the proposed framework. The proposed dataset selection customization mechanism and the proposed DRGM are helpful in guiding non-experts in identifying the data requirements for machine learning systems tailored to a specific ML problem. This approach also aids in evaluating different dataset alternatives to choose the optimum dataset for the problem. For future work, we recommend implementing tool support to generate the DRGM based on a chatbot interface.