Traceability links between requirements and source code are crucial for maintaining software quality, yet traditional traceability methods exhibit critical limitations. Current methods predominantly rely on semantic similarity among artifacts, failing to account for link transitivity and the underlying rationale governing traceability link formation. To address these challenges, this study introduces HGTRTracer, a novel framework that integrates Large Language Models (LLMs) with Graph Neural Networks (GNNs) to advance traceability analysis. The proposed method innovatively incorporates attribution reasoning via LLMs to derive explicit “Reason nodes” that justify traceability links, with a Heterogeneous Graph Transformer (HGT) captures structural dependencies among nodes and edges to improve link prediction accuracy. Experiments conducted on 10 open-source projects demonstrate that HGTRTracer outperforms the state-of-the-art baseline (HGNNLink) by 12.90% in F1-score on average. Beyond performance gains, the framework enhances interpretability through its attribution-aware design, providing actionable insights for traceability decisions.

Graph Convolutional Networks (GCNs) demonstrate significant potential in recommendation systems but face difficulties with the cold-start problem, especially in integrating new nodes during inference. The typical solution leverages meta-learning for few-shot learning, though it often fails to fully capture collaborative filtering between nodes. In this paper, we revisit the node embedding propagation algorithm in GCNs, emphasizing the importance of collaborative filtering and elucidating the relation between high-order and low-order embeddings. Given the substantial interaction data required for training recommendation models, maintaining a simple model structure remains crucial. To address these challenges, we propose the Linear Graph Network (LGNet), which theoretically compresses multi-layer GCNs into a single layer, enabling the embedding of new nodes during inference. Experimental results on benchmark datasets for link prediction and user cold-start tasks demonstrate that LGNet outperforms existing methods. The code will be available at https://github.com/kunbeibei/LGNet.

Current research on requirements-to-code traceability predominantly focuses on improving the performance of algorithms or models, while neglecting the quality of project artifacts themselves.Nevertheless, high-quality data can significantly improve the performance of a model, and disregarding data quality can be challenging to rectify even with sophisticated models.Often, high-quality data contributes substantially more to model performance enhancement than improvements to the model's structure itself.In real-world software development, requirements and code artifacts are the primary data sources for establishing traceability links.However, there is a lack of effective guidance on what factors of requirements and code artifacts are conducive to traceability.To address this issue, this study proposes five metrics that are verified to have a close impact on the requirementsto-code traceability: cyclomatic complexity, annotation ratio, co-occurrence ratio, noun-verb ratio, and noise ratio.Based on a systematic mapping study, we selected 11 open-source projects from 20 relevant publications to conduct experiments, and employed Spearman correlation analysis to validate the association between the proposed metrics and traceability.The experimental results show that the proposed five metrics can significantly affect the traceability of requirements-to-code, of which the cyclomatic complexity and the annotation ratio are more influential.

In the field of software traceability (ST), machine learning (ML) has become a common and effective method for automated issue-commit link recovery. The features extracted from issue and commit artifacts are composed of significantly different types of data, such as issue summaries, diff codes, and hashes. Such complex and diverse data poses a challenge to conventional ML methods. To overcome this challenge, we propose a novel model named FRELinker, which trains independent classifiers based on the type of issue-commit training data, fully leveraging the effectiveness of ML for single type data, and then fuses the classifiers. Specifically, we categorize the features into four types: textual features, code features, non-textual features, and similarity features, and extend text similarity features by adding hybrid textual similarity measures. And then, we use a ranking method to select the optimal classifiers corresponding to these four types of features. Among them, the optimal classifier for textual features is Gradient Boosting (GB), the optimal classifier for code features is Logistic Regression (LR), and the optimal classifier for non-textual features and similarity features is Random Forest (RF). Finally, we use a Bayesian optimization model to fuse these four classifiers. Experimental results show that our method outperforms competing methods Hybrid-Linker and DeepLink in terms of Precision, Recall, and F-measure on six real-world open-source software (OSS) datasets, demonstrating significant performance advantages in complex and diverse data.

In the task of requirements-to-code traceability link recovery, the continues growth of software scale has led to diminishing differences between indices and more complex nonlinear relationships within the data. This results in the performance decline of the most widely used information retrieval methods and machine learning methods when handling this task. Therefore, we propose a requirement traceability method based on heterogeneous graph attention networks, named HANTracer. The model integrates the high-dimensional vectors generated by a pre-trained model as node features to deepen the dif-ferentiation between nodes and enhances node representations with contextual information learned from the graph structure. Additionally, it utilizes the properties of heterogeneous edges to construct various edge features, such as code calling, code inheritance, and text similarity relationships, aiding the model in understanding and utilizing the relationships between different types of data elements. By incorporating average pooling layers and multiple fully connected layers, the HANTracer model is further improved to enhance its ability to extract nonlinear features. Experimental results indicate that HANTracer achieves an average F1 performance higher than the state-of-the-art methods TAROT by 100.30% and DF4RT by 46.27% on seven real-world open source software (OSS) datasets, demonstrating significant performance advantages in large-scale and complex data environments.

In recent years, Prompt Learning, based on pre-training, prompting, and prediction, has achieved significant success in natural language processing (NLP). The current issue-commit link recovery (ILR) method converts the ILR into a classification task using pre-trained language models (PLMs) and dedicated neural networks. However, due to inconsistencies between the ILR task and PLMs, these methods not fully leverage the semantic information in PLMs. To imitate the above problem, we make the first trial of the new paradigm to propose a Multi-template prompt learning method with adversarial training for issue-commit link recovery (PromptLink), which transforms the ILR task into a cloze task through the template. Specifically, a Multi-template PromptLink is designed to enhance the generalisation capability by integrating various templates and adopting adversarial training to mitigate the model overfitting. Experiments are conducted on six open-source projects and comprehensively evaluated across six commonly measures. The results show that PromptLink achieves an average F1 of 96.10%, Precision of 96.49%, Recall of 95.92%, MCC of 94.04%, AUC of 96.05%, and ACC of 98.15%, significantly outperforming existing state-of-the-art methods on all measures. Overall, PromptLink not only enhances performance and generalisation but also emerges new ideas and methods for future research. The source code of PromptLink is available at https://figshare.com/s/6130d42ff464c579cdec.

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.

Traceability refers to the potential for traces to be established (i.e., created and maintained) and used. Bug Traceability (BT) is critical for enhancing software quality, reducing maintenance costs, and boosting team efficiency. To explore the trends and advancements of BT, we conduct a systematic mapping study (SMS). We initially retrieve 4674 citations from 7 databases spanning 2014 to 2023, and 24 primary studies meet the rigorous selection criteria. Our study identifies 6 types of bug trace links, 8 traceability strategies, and 47 bug traceability recovery (BTR) techniques. Among them, 47 BTR techniques can be further classified into 6 categories. At the same time, we perform statistics on 113 datasets and 16 evaluation metrics used to assess the performance of BTR techniques proposed in the primary studies. In evaluating the overall quality of the primary studies, 8 dimensions are utilized to support technology transfer, categorizing the overall quality into 4 levels: poor, middle, good, and excellent, with 79% of primary studies evaluated at a good level. This study not only furnishes a clear definition of BT for scholarly reference, but also highlights that information retrieval (IR), machine learning (ML) and deep learning (DL) techniques are the mainstream techniques used for BTR.

Information Retrieval (IR), Machine Learning (ML), and Deep Learning (DL) have become mainstream methods for traceability link recovery. However, IR-based methods face the challenge of low precision, while DL-based methods require large-scale training data to achieve better performance. In this paper, we propose a novel model XWCoDe, which apply XGBoost combined with a weighted code dependency strategy to traceability link recovery domain. In order to refine the initial candidate links generated by the XGBoost model, the strategy only modifies low confidence candidate links and pioneers the use of graph embedding technology node2vec to calculate the importance of each code dependency relationship. The experimental results show that the average F1 score of XW CoDe on 4 datasets and 9 training/testing ratios is 12.93 % higher than the state-of-the-art method DF4RT.

Adversarial examples pose significant challenges for Natural Language Processing (NLP) model robustness, often causing notable performance degradation. While various detection methods have been proposed with the aim of differentiating clean and adversarial inputs, they often require fine-tuning with ample data, which is problematic for low-resource scenarios. To alleviate this issue, a Subspace Clustering based Adversarial Detector (termed SCAD) is proposed in this paper, leveraging a union of subspaces to model the clean data distribution. Specifically, SCAD estimates feature distribution across semantic subspaces, assigning unseen examples to the nearest one for effective discrimination. The construction of semantic subspaces does not require many observations and hence ideal for the low-resource setting.

The escalating growth of network technology and users poses critical challenges to network security. This paper introduces ANDE, a novel framework designed to enhance the classification accuracy of anonymity networks. ANDE incorporates both raw data features and statistical features extracted from network traffic. Raw data features are transformed into images, enabling recognition and classification using robust image domain models. ANDE combines an enhanced Squeeze-and-Excitation (SE) ResNet with Multilayer Perceptrons (MLP), facilitating concurrent learning and classification of both feature types. Extensive experiments on two publicly available datasets demonstrate the superior performance of ANDE compared to traditional machine learning and deep learning methods. The comprehensive evaluation underscores ANDE’s effectiveness in accurately classifying network traffic within anonymity networks. Additionally, this study empirically validates the efficacy of the SE block in augmenting the classification capabilities of the proposed framework, establishing ANDE as a promising solution for network traffic classification in the realm of network security.

In software stores, user reviews are the most direct feedback on product experiences and requirements. Sentiment analysis (SA) technology has been proven to be efficient for review mining-based requirements elicitation. Our objective is to identify the trend and actuality in SA for requirements elicitation from app reviews. We conduct a systematic mapping study, retrieving 740 citations from 2013 to 2023, and 33 articles are retained as primary studies. The overall research posture is increasing yearly. There are 24 self-crawled and seven specially created datasets. SVM is the most commonly used classifier, while BERT represents the most innovative algorithm with a good performance. Precision, recall, and F -measure are the most popular metrics. An SA framework based on feature granularity is proposed to guide newcomers in this field. It is proven that SA can effectively assist in transitioning from traditional methods to review mining in requirements elicitation. Overall, SA for requirements elicitation from app reviews is becoming an increasingly mature cross-research field. Future research can focus on building highly generalized and multilingual datasets, using more fine-grained feature extraction methods to improve requirements elicitation accuracy, and exploring BERT families or other attention-based deep learning models.

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.

Abstract Requirements management (RM) plays an important role in requirements engineering. The development of machine learning (ML) is in full swing, and many ML software management techniques had been used to improve the performance of RM methods. However, as no research study is known that exists systematically to summarise the ML methods used in RM. To fill this gap, this paper adopts the systematic mapping study to survey the state‐of‐the‐art ML methods for RM primary studies and were finally selected in this mapping, which was published on 36 conferences and journals. The 24 factors affecting the ML method of RM are determined, of which 9, 11 and 4 are the three parts of RM, namely requirements baseline maintenance, requirements traceability and requirements change management separately. The 18 objectives of the ML method for RM are summarised, of which 6, 7 and 5 are the three parts of RM. The eight ML methods used in RM and their time sequence are summarised. The 18 evaluation indexes for RM in the ML method are determined, and the performance of these methods on these parameters is analysed. The research direction of this paper is of great significance to the research of researchers in demand management.

As Vehicular Ad-hoc Networks (VANETs) bring fantastic revolution to intelligent transportation systems, their own security has become an important research topic. However, authentication security, as the key issue of VANETs’ security, is still facing great challenges. Therefore, this survey first starts with the background of VANETs and then introduces the main security concerns. To distinguish from existing surveys, this paper proposes the security challenges and security properties of VANETs from the perspective of builders and attackers, respectively. Then, we present the necessary and important characteristics of a VANET’s security system including the authenticity of nodes and information, the availability of network systems, the integrity and confidentiality of information, and the non-repudiation of information after transmission. Specifically, attack methods and detection schemes for these characteristics are highlighted in detail and analyzed in terms of their advantages and limitations, which fill the gaps in the existing survey. More importantly, we focus on the authentication schemes proposed in recent years, reporting the latest advances in VANETs. These schemes are analyzed and compared in depth in terms of the security characteristics and attack resistance of authentication, as well as in terms of overhead and efficiency. Finally, this paper summarizes some lessons and discusses several future research directions.

Entities and relations extraction is one of the key task to build medical knowledge graph, which is of great significance to the development of medical artificial intelligence. However, overlapping triples are great challenge for biomedical entities and relations extraction. In order to improve the performance of biomedical entities and relations extraction, we propose a joint extraction of entities and relations method based on decomposition and recombination strategy to mine biomedical text. Our method decomposes entities and relations extraction task into three related sub-modules, which are entity tagging module, relation classification module and recombination matching module. Our main contributions are as follows: first, we introduce a decomposition and recombination end-to-end learning framework for joint entities and relations extraction. Second, we propose a bi-directional prediction method to deal with the overlapping triples problem. Finally, we propose the negative samples generation method to alleviate the error accumulation among these modules. The extensive experiments demonstrate that our method can improve the F1 score by 4.36%, 2.13% and 11.72% in ADE, DDI and BB biomedical corpus.

In information retrieval-based (IR-based) requirements traceability research, a great deal of researches have focused on establishing trace links between requirements and source code. However, as the description styles of source code and requirements are very different, how to better preprocess the code is crucial for the quality of trace link generation. This paper aims to draw empirical conclusions about code feature extraction, annotation importance assessment, and annotation redundancy removal through comprehensive experiments, which impact the quality of trace links generated by IR-based methods between requirements and source code. The results show that when the average annotaion density is higher than 0.2, feature extraction is recommended. Removing redundancy from code with high annotation redundancy can enhance the quality of trace links. The above experiences can help developers to improve the quality of trace link generation and provide them with advice on writing code.

Context: Requirements trace recovery (RTR) is always time-consuming, tedious, and fallible.There has been a growing interest in applying information retrieval (IR) to automate the process of recover trace links between requirements artifacts and other software artifacts.Objective: In this review, our objective is to identify the state-of-the-art of how IR has been explored to automate RTR and provide an overview of the research at the intersection of these two fields.Method: A systematic mapping study has been conducted, searching the main scientific databases.The search retrieved 1587 citations and 34 articles are retained as primary studies.Results: The results show the most active authors and publication distribution.It presents four kinds of IR models and 21 enhancement strategies applied to perform RTR.Besides, the lists of 37 experimental datasets and 9 measures, commonly used together to evaluate IR-based RTR approaches, are provided.Conclusions: Vector Space Model (VSM) and Latent Semantic Index (LSI) are the most two studied IR models used in RTR.CoEST becomes the most popular, convenient and stable source of datasets.Precision and Recall are the most common measures used to evaluate the performance of IR methods.Overall, IR-based RTR is becoming an increasingly mature cross research field.

Image event recognition is different from object recognition, behaviour recognition and scene recognition. Event is a more advanced concept than object, behaviour and scene. Regarding semantics loss in image event recognition, this paper first proposes a WordNet-based optimization algorithm for concept semantics similarity and describes the semantics relations between different concepts by taking account of such following four impact factors in the WordNet tree as concept semantics distance, concept node depth, concept node density and concept semantics overlap ratio. On that basis, an image event recognition algorithm (CS-IER) based on concept score is proposed, while multi-view learning is applied to fuse concept score and inter-conceptual semantics relations. However, if a higher erroneous concept score is given using CNN, multi-view learning will also augment the concept score approximate to its erroneous concept semantics, thereby leading to the distortion of image representation information. To address this problem, CNN is used to extract channel information to obtain the local features of the image, and it is further fused with the optimized concept score features, so as to form the final image representation information and complete the image event recognition. In experiments, the effectiveness of the proposed algorithm on three datasets is verified.

The approaches based on laboratory experiments to explore the interactions between viruses and their hosts are costly and time-consuming. Due to advances in high-throughput technologies, recent computational methods to predict virus-host interaction have attracted increasing attention. But these methods couldn’t effectively utilize the heterogeneous information of the virus-host interaction network. In this paper, we propose a computational method to predict potential virus-host interaction via network fusion and graph mining, named VHINFGM. Different from existing methods, VHINFGM constructs two different heterogeneous networks from the existing interaction network through similarity network fusion and graph embedding technique. Then, VHINFGM introduces two kinds of meta-path scores to extract features from each heterogeneous graph. Based on this graph mining approach, a mixed feature vector for two heterogeneous networks can be obtained, which can be used as the input of a classifier to predict potential interactions. VHINFGM is verified on four datasets, it can be found that VHINFGM outperforms the state-of-the-art methods. 5 out of the top 10 virus-host interaction reported by VHINFGM has been validated in the biological experiments. Besides, VHINFGM predicts 5 new virus-host relationships, which could guide further research.

Trace links between requirements and software artifacts provide available traceability information and in-depth insights for different stakeholders. Unfortunately, establishing requirements trace links is a tedious, labor-intensive and fallible task. To alleviate this problem, Information Retrieval (IR) methods, such as Vector Space Model (VSM), Latent Semantic Indexing (LSI), and their variants, have been widely used to establish trace links automatically. But with the widespread use of agile development methodology, artifacts that can be used to generate automatic tracing links are getting shorter and shorter, which decreases the effects of traditional IR-based trace link generation methods. In this paper, Biterm Topic Model–Genetic Algorithm (BTM–GA), which is effective in managing short-text artifacts and configuring initial parameters, is introduced. A hybrid method VSM[Formula: see text]BTM–GA is proposed to generate requirements trace links. Empirical experiments conducted on five real and frequently-used datasets indicate that (1) the hybrid method VSM+BTM[Formula: see text]GA outperforms the others, and its results can achieve the “Good” level, where recall and precision are no less than 70% and 30%, respectively; (2) the performance of the hybrid method is stable and (3) BTM–GA can provide a number of “hard-to-find” trace links that complement the candidate trace links of VSM.

Trace links between software artifacts provide available traceability information and in-depth insights for different stakeholders.Unfortunately, establishing trace links is a fallible, tedious, and labor-intensive task.To alleviate these problems, many Information Retrieval (IR) methods, such as Vector Space Model (VSM), Latent Semantic Indexing (LSI) and their variants, have been proposed to establish trace links automatically.In recent years, short-text artifacts (or even lack of documentation) become a new trend as more and more software systems are developed abiding by agile methodologies.It makes the effects of traditional IR-based trace links generation methods even worse.In this paper, Biterm Topic Model (BTM), which is good at dealing with short text, is introduced to solve the problem.A hybrid method combining VSM and BTM is proposed to generate requirements trace links.The empirical experiments conducted on three real and frequently-used datasets indicate that the hybrid method can achieve better performance, and the results can reach the "acceptable level" directly.

The ultimate goal for researchers to develop a requirements traceability (RT) technology is transferring it from academy to industry. Academic researchers and industrial practitioners both expect that more and more technologies can be transferred. However, there is always a wide gap between RT research and practice. In this thesis, I'll propose a novel model that focuses on evaluating the RT technologies transfer maturity. A reasonable maturity evaluation model is proposed to measure to what extent an academic RT technology must achieve before it is able to be transferred to industry. Based on the model, an initial progress is made in selecting a suitable RT technology for industrial practitioners.