Mohammad Amin Zadenoori

Mohammad Amin Zadenoori, Waad Alhoshan, Jacek Dąbrowski et al.

Large language models (LLMs) are increasingly applied to requirements engineering (RE) tasks, yet the prompts guiding them are typically designed manually through trial and error, yielding inconsistent and suboptimal results. Automated prompt construction remains largely unexplored in RE, leaving its effectiveness unclear. To address this, we propose a lightweight Automatic Prompt Engineering approach, Backtracking APE (BT-APE), and apply it to requirements classification. We frame prompt design as an optimization problem, iteratively refining prompts via LLM-generated candidates, backtracking search, and dynamic example selection. Evaluating BT-APE on three benchmark datasets with five instruction-tuned LLMs, we compare it against four classical prompting baselines (zero-shot, few-shot, chain-of-thought, CoT+few-shot) and a state-of-the-art but resource-intensive APE baseline (PE2). BT-APE and PE2 achieve nearly identical accuracy, both substantially outperforming the classical baselines with large effect sizes; however, BT-APE imposes a far lighter computational footprint, consuming roughly 72% fewer input tokens and 66% less wall-clock time at equivalent accuracy, making it better suited to resource-constrained deployment. Our contributions are threefold: (i) a lightweight APE framework with an open interactive tool and replication package; (ii) the first systematic comparison of APE against classical prompting for requirements classification; and (iii) insights into how class definitions and prompt evolution affect performance.

Mohammad Amin Zadenoori, Vincenzo De Martino, Jacek Dabrowski et al.

[Context and motivation] Large language models (LLMs) show notable results in natural language processing (NLP) tasks for requirements engineering (RE). However, their use is compromised by high computational cost, data sharing risks, and dependence on external services. In contrast, small language models (SLMs) offer a lightweight, locally deployable alternative. [Question/problem] It remains unclear how well SLMs perform compared to LLMs in RE tasks in terms of accuracy. [Results] Our preliminary study compares eight models, including three LLMs and five SLMs, on requirements classification tasks using the PROMISE, PROMISE Reclass, and SecReq datasets. Our results show that although LLMs achieve an average F1 score of 2% higher than SLMs, this difference is not statistically significant. SLMs almost reach LLMs performance across all datasets and even outperform them in recall on the PROMISE Reclass dataset, despite being up to 300 times smaller. We also found that dataset characteristics play a more significant role in performance than model size. [Contribution] Our study contributes with evidence that SLMs are a valid alternative to LLMs for requirements classification, offering advantages in privacy, cost, and local deployability.