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Requirements-to-code traceability link recovery (RC-TLR) can establish connections between requirements and target code artifacts, which is critical for the maintenance and evolution of large software systems. However, to the best of our knowledge, there is no existing experimental study focused on state-of-the-art (SOTA) methods for the RC-TLR problem, and there is also a lack of uniform benchmarks for evaluating new methods in the field. We developed a framework to identify SOTA methods using the Systematic Literature Review method and applied it to research in the RC-TLR field from 2018 to 2023. Through experiments replication on 13 datasets using 6 methods, we observed that for information retrieval-based methods, Close Relations between Target artifacts-based method (CRT), TraceAbility Recovery by Consensual biTerms (TAROT), and Fine-grained TLR (FTLR) performed well on COEST dataset, while Combining Part-Of-Speech with information-retrieval techniques (Conpos) and TAROT achieve promising results in large datasets. As concerns machine learning-based methods, Random Forest consistently exhibits strong performances on all datasets. We hope that this study can provide a comparative benchmark for performance evaluation in the RC-TLR field. The resource repository that we have established is expected to alleviate the workload of researchers in performance analysis, and promote progress of the field.

Traceability links between issues and commits (issue-commit links recovery (ILR)) play a significant role in software maintenance tasks by enhancing developers’ observability in practice. Recent advancements in large language models, particularly pre-trained models, have improved the effectiveness of automated ILR. However, these models’ large parameter sizes and extended training time pose challenges in large software projects. Besides, existing methods often overlook the association and distinction among artifacts, leading to the generation of erroneous links. To mitigate these problems, this paper proposes a novel link recovery method called MTLink. It utilizes multi-teacher knowledge distillation (MTKD) to compress the model and employs an adaptive multi-task strategy to reduce information loss and improve link accuracy. Experiments are conducted on four open-source projects. The results show that (i) MTLink outperforms state-of-the-art methods; (ii) The multi-teacher knowledge distillation maintains accuracy despite model size reduction; (iii) The adaptive multi-task tracing method effectively handles confusion caused by similar artifacts and balances each task. In conclusion, MTLink offers an efficient solution for ILR in software traceability. The code is available at https://zenodo.org/records/10321150.