ABSTRACT Introduction Requirements engineering plays a crucial role in the software development lifecycle, encompassing the elicitation, analysis, specification, and validation of requirements. Inefficiencies in any of these processes can lead to delays, budget overruns, and even project failure. This paper explores the integration of requirements reuse and recommender systems to enhance the elicitation process by leveraging historical project data and stakeholder interaction patterns. Methods The proposed methodology incorporates a hybrid approach that combines collaborative filtering and content‐based filtering to recommend relevant requirements to stakeholders. A dynamic weighting framework adjusts the contributions of these two approaches based on the availability of data. In situations with insufficient qualified data, the approach relies more heavily on content‐based filtering to address challenges such as data sparsity and the cold‐start problem. To enhance the semantic similarity between requirements, the method aggregates GloVe word vectors with domain‐specific TF‐IDF scores to identify software engineering‐specific vocabulary. Results Experimental evaluation using a benchmark dataset demonstrates that the proposed hybrid approach significantly improves the prediction accuracy of relevant requirements recommendations, compared to traditional methods. Conclusion The integration of requirements reuse with a recommender system that combines collaborative and content‐based filtering offers an effective solution to streamline the elicitation process, mitigate risks of overlooking critical requirements, and save time during the evaluation and selection of requirements. The proposed method improves the efficiency and accuracy of requirements engineering, especially in contexts with limited data availability.

The space environment has become more congested and competitive than ever before, making collision-free satellite operation a top priority for both public and private sectors. Specifically, effective collision avoidance involving Residence Space Objects (RSOs) stands out as a critical challenge within the domain of Space Situational Awareness (SSA). This paper addresses this issue by exploring the application of Reinforcement Learning (RL) methods, which shows promising results in addressing the complexities of satellite path planning and collision avoidance. This paper focuses on single-object maneuver planning, where the primary satellite aims to avoid collisions with secondary objects using the Proximal Policy Optimization (PPO) algorithm. The obtained results demonstrate the promising performance of the trained model, establishing a proof of concept for further investigation of RL for maneuver planning and collision avoidance in space.

Many developers rely on Large Language Models (LLMs) to facilitate software development. Nevertheless, these models have exhibited limited capabilities in the security domain. We introduce LLMSecGuard, a framework to offer enhanced code security through the synergy between static code analyzers and LLMs. LLMSecGuard is open source and aims to equip developers with code solutions that are more secure than the code initially generated by LLMs. This framework also has a benchmarking feature, aimed at providing insights into the evolving security attributes of these models.