In recent years, increasing machine learning has been applied to Requirements Traceability Recovery (RTR). The performance of these approaches is not satisfactory. Besides, most of them perform well in recovering traceability links between specific artifacts but cannot maintain consistent performance in different scenarios. To alleviate this problem, we propose a novel approach based on Multi-Layered Gradient Boosting Decision Trees (mGBDT) for RTR which is called MLTracer. MLTracer stacks several GBDTs and learns hierarchical representations of artifact link features. Through layer-by-layer training, it adapts to the feature distribution in different scenarios to improve generalization ability. MLTracer is evaluated on five software projects and compared with six state-of-the-art approaches. The results indicate that MLTracer achieves an average F1 score of 0.6153, outperforming six baseline approaches across all datasets. It proves that MLTracer achieves state-of-the-art performance and has strong applicability in actual industry.

Machine learning (ML) has been widely used in trace link recovery (TLR) to reduce the manual maintenance cost of trace links by developers. However, the imbalanced distribution of valid links and invalid links seriously affects the performance of classifiers. Although a few studies have applied data balancing techniques (DBT) to ML-based TLR, none of them has systematically analyzed more effective combinations of them. Therefore, we perform an empirical study on three groups of control experiments to explore the impact of the combination of different ML methods with and without DBT on TLR efficiency. We compare the performance of supervised ML-based TLR and unsupervised ML-based TLR with and without DBT respectively. Then, we analyze the performance of the ensemble learning model (EM) with DBT on TLR. The experimental results on the 7 imbalance datasets of CoEST indicate that DBT has a positive effect on ML-based TLR. Specifically, the recall of the LR model increased by 0.5517 after combining with most DBTs on EasyClinic(ID-TC), while Tomek-link significantly improves the precision of K-Nearest Neighbor (KNN), Decision Tree (DT), LR, Support Vector Machine (SVM). The precision of LR increased from 0.5036 to 1.0. BalanceRF is best at increasing recall, reaching 1.0 on 4 datasets. Moreover,the improvement degree of ML-based TLR with DBT shows differences in terms of the size of datasets and the proportion of valid links.

Requirements traceability (RT) is crucial for requirement management and impact analysis of requirement change in software development.The applications of machine learning (ML) technologies to RT have received much attention.In this paper, we aim to provide the state-of-the-art progress of the studies on the intersection of ML and RT.A systematic mapping study (SMS) is conducted and 26 studies have been identified as primary studies.The results present 32 ML technologies and 7 enhancement strategies for establishing trace links.Besides, 46 datasets are utilized for validating the performance of these ML technologies.Additionally, the overall quality of these primary studies is at a good level.This study indicates that numerous studies have proved the potential of utilizing ML technologies for predicting emerging trace links in RT by utilizing existing traceability information.Moreover, open-source datasets are the most popular, which greatly improves the reproducibility of studies.However, there is still a gap between academia and industrial application because of the lack of industrial practice and guidance from practitioners.

Nowadays, many supervised learning techniques have been applied to requirements traceability recovery (RTR). However, the performance of these supervised learning techniques is still far from satisfactory, and exploring a more effective model is necessary. This paper proposes a new deep forest model for RTR(DF4RT) with a novel composition to improve the model's performance. The proposed model incorporates three feature representation methods, which not only information retrieval and query quality but also add distance. The DF4RT model is evaluated on four open-source projects and compared with nine state-of-the-art tracing approaches. The experimental results show that DF4RT improves precision by 94%, recall by 58%, and F-measure by 72% on average. We also conduct ablation experiments to explore the impact of the different features. It is the first time that deep forest is employed in requirements traceability. Our approach is effective for RTR with good interpretability, few parameters, and good performance in small-scale data.

Context: Software traceability (ST) refers to capturing associations in various artifacts. A growing interest has been in applying machine learning (ML) techniques to ST. Objective: The purpose of this work is to present a comprehensive review of the state-of-the-art progress on the intersection of ML and ST. Method: A systematic mapping study (SMS) is conducted. A total of 965 citations are retrieved from 2013 to 2022, among which 37 studies are selected as primary studies. Result: 32 ML technologies and 9 enhancement strategies for generating trace links have been identified. Besides, 90 datasets and 16 measures have been summarized, which are applied to evaluate the efficacy of the ML-based tracing techniques. The overall reproducibility of these primary studies is at a medium level. Conclusion: We have found that ML is playing a positive role in improving the accuracy and efficiency of ST. However, there are still some challenges such as reproducibility. Hence, researchers are suggested to pay more attention to standardization to improve the reproducibility of studies.