Leveraging the code comprehension capabilities of Large Language Models (LLMs), we propose an automated approach to enhance Requirement-to-Code (R2C) traceability through Chain-of-Thought (CoT) prompting. Traditional Information Retrieval (IR) techniques (e.g., VSM, LSI) exhibit limited accuracy due to the semantic gap between natural language requirements and syntactic code structures. Our framework addresses this gap via two key innovations: (1) CoT-guided generation of functional code summaries through iterative reasoning steps (language identification, comment analysis, naming pattern interpretation), and (2) a similarity-based reordering strategy utilizing SimCSE embeddings to refine candidate links. Evaluations on four industrial datasets (iTrust, eTour, eANCI, SMOS) show $\mathbf{1 2 5. 4 7 \%,} \mathbf{6 3. 2 \%}$ and $\mathbf{6 2. 1 3 \%}$ average MAP improvements over LSI, FTLR+ and FQETLR+ baselines respectively. The approach demonstrates particular strength in low-coverage scenarios (106.19% average MAP improvement on eANCI). This study establishes novel approaches for LLM-driven traceability link recovery and provides actionable insights for integrating prompt engineering into software lifecycle tools.

ABSTRACT As software development grows increasingly complex, application programming interface (API) plays a significant role in enhancing development efficiency and code quality. However, the explosive growth in the number of APIs makes it impossible for developers to become familiar with all of them. In actual development scenarios, developers may spend a significant amount of time searching for suitable APIs, which could severely impact the development process. Recently, the OpenAI's large language model (LLM) based application—ChatGPT has shown exceptional performance across various software development tasks, responding swiftly to instructions and generating high‐quality textual responses, suggesting its potential in API recommendation tasks. Thus, this paper presents an empirical study to investigate the performance of ChatGPT in query‐based API recommendation tasks. Specifically, we utilized the existing benchmark APIBENCH‐Q and the newly constructed dataset as evaluation datasets, selecting the state‐of‐the‐art models BIKER and MULAREC for comparison with ChatGPT. Our research findings demonstrate that ChatGPT outperforms existing approaches in terms of success rate, mean reciprocal rank (MRR), and mean average precision (MAP). Through a manual examination of samples in which ChatGPT exceeds baseline performance and those where it provides incorrect answers, we further substantiate ChatGPT's advantages over the baselines and identify several issues contributing to its suboptimal performance. To address these issues and enhance ChatGPT's recommendation capabilities, we employed two strategies: (1) utilizing a more advanced LLM (GPT‐4) and (2) exploring a new approach—MACAR, which is based on the Chain of Thought methodology. The results indicate that both strategies are effective.

Review comment generation is a crucial task in code review, and significant progress has been made in automating. Previous research has generated review comments by fine-tuning pre-trained models or Large Language Models (LLMs). However, these studies have overlooked the necessity of conducting code reviews from multiple perspectives, resulting in the omission of potential issues in code changes. Additionally, the complexity of review comments often hinders the accurate quantitative evaluation of automated tools’ effectiveness.In this paper, we first conduct an empirical study to propose a comprehensive taxonomy of code review dimensions. We also identify three major limitations of existing automated code review (ACR) methods: lack of comprehensiveness, incorrectness, and vagueness. Building on the insights from our empirical study, we introduce Hydra-Reviewer, a collaborative multi-agent framework powered by large language models, designed to automatically generate high-quality code reviews. We utilize the CodeReview and CodeReviewNew benchmark datasets, along with a newly constructed review comment generation dataset. We compare Hydra-Reviewerwith several baselines, including CodeReviewer, LLaMA-Reviewer, ChatGPT, Comprehensive-ChatGPT, and DeepSeek-V3.The experimental results show that Hydra-Reviewerachieves a BLEU score of 8.20, outperforming the state-of-the-art baseline, DeepSeek-V3, which scores 7.85. In qualitative evaluation, Hydra-Reviewer’s generated comments span an average of 7.8 review dimensions, addressing the limitations of existing ACR methods effectively. Additionally, Hydra-Reviewerdemonstrates strong generalization capabilities on unseen dataset. We further validate the contributions of each component of Hydra-Reviewerthrough an ablation study and confirm the helpfulness and readability of the generated comments via a User Study. Finally, a cost analysis reveals that Hydra-Reviewergenerates review comments at an average cost of 0.018 dollars and 62.63 seconds per code change.

Cloud computing is an Internet-based computing. It relies on sharing computing resources which are delivered as services on the Internet. Web service is one of the most important types of services that can be used in cloud computing. But many of them may be similar in some functional or nonfunctional properties, making how to recommend a suitable web service a problem facing many developers. Researchers have taken the QoS attributes into consideration. However, their research is on the premise that all the recommended web services are compatible, i.e., the recommended web services can be composed with existing web services. It may not always be true. In this paper, we only take the compatibility of web services into consideration, and present a BIpartite Graph based Service Recommendation (BIGSIR) method to address the service compatibility problem. BIGSIR uses the historical usage data of web services to recommend web services to developers. Different from existing web service recommendation approaches, BIGSIR adopts a bipartite graph to visual the web services and the relationship between them. Based on the graph model, an effective recommendation algorithm is introduced to recommend the suitable web services. Our approach is evaluated on a dataset constructed from myExperiment, a search engine that contains about 1, 851 web services and 2, 000 workflows. Experimental results demonstrate that apart from some isolated web services or workflows, BIGSIR can obtain promising results. And we also explore the factors that will influence the performance of BIGSIR. This work not only provides a new dataset, but also highlights a new perspective for service recommendation, i.e. services as a bipartite network.

Folksonomies enable Internet users to share, annotate and search for online resources with tags. Tag recommendation can suggest tags that maximize utility and help to promote resource sharing and collaboration. To date, little has been done to collaboratively recommend tags in narrow folksonomies. In this paper we apply K-Nearest Neighbor to find similar users with common interests who lie close to each other in social networks. We present a novel tag recommendation strategy in social comment context. The evaluation shows that tag recommendation based on local lexicon is an effective way to improve collaborative resource sharing in social networks.

It is important that a collaborative graphics editing system (CGES) allows a latecomer to join the collaborative editing session as soon as possible. In ubiquitous environment, a powerful workstation may most probably join and take the place of former server in order to take the advantage of the centralized architecture and make the collaborative editing more efficiently. We have developed a latecomer accommodation service and related late-join approach for such kind of latecomer in CGES with the centralized architecture. The presented on-demand input replica consistency approach and related object scheme ensure that only those input operations really needed for the latecomer are transmitted. The region that late joiner may be interested in are predicted. The needed partial input replica is migrated only when it is needed. The advantages of the proposed late-join approach are robustness, a low network load and a low initialization delay. The late-join approach is realized in our prototype system of collaborative graphics editing system that can work well in ubiquitous environments.

Rapid advances in the enabling technologies for mobile and ubiquitous computing help portable devices to join collaborative graphics design conveniently. The limitation of display size and computational power of these embedded devices makes it hard for mobile users to browse large pattern that renewed in real time efficiently. We present a novel user interest region prediction algorithm and related system that use state duration based segmental probability model (SDSPM) to forecast user's intention in the near future. Related experiment was carried out to test the effectiveness of the algorithm. Based on the prediction, only sub-patterns that might be interested to user are issued to the embedded sites. The proposed user interest prediction system is effective and the feasibility of the collaborative graphics design system in ubiquitous environment is enhanced.

A well arranged task priority in dual-shore collaborative software design phase will smooth the collaboration and reduces the potential coordination lag. However, it is really difficult to determine the priority when there are many collaboration tasks and the collaborative process is a little complicated. On the basis of behavior compatibility analysis using Petri-net, this paper proposed yet a new automatic method to determine the priority. The collaborative design is modeled in Petri-net and some algorithms are presented for the automatic priority determining.

Collaborative graphics design systems allow users, spread across different locations, to work together designing common patterns. One key element that, in most cases, is poorly supported in such systems is semantic preservation. To make the real-time collaborative graphics design system more efficient, it is essential that the semantic violation problem be well solved. In this paper we present the semantic maintenance architecture and the classification of semantic preservation. A new approach and related algorithm that can resolve semantic conflict problem efficiently is also proposed. Semantic consistency maintenance helps to promote the collaborative awareness abilities. The architecture and algorithm has been tested in the CoDesign prototype system.

Coordination Mechanism is a critical issue in the cooperative work. However, there are no universal and efficient coordination mechanisms that are fit for the collaborative graphic designing system accounts for it’s special characteristic and properties. In this paper we propose a novel intelligent coordination mechanism, which enable members to concentrate on the most of controversial part of the cooperative work by utilizing human awareness information. Related algorithm that includes two key concepts: expectation and convergence is proposed. The mechanism and algorithm have been realized in our prototype system and are proved significantly promote the coordination efficiency.

Video-based channel that can transmit people's facial expression information is designed to support real-time interaction between collaborators. Yet exchanging facial video in ubiquitous environment is confined by the low bandwidth and limited computational resources on mobile embedded devices. In this paper, we present a novel two dimensional (2D) facial animation communication system which enables the embedded sites to gain awareness of PC users' facial expression via facial animation. The system was examined by experiments and survey. Results show that 2D facial animation provides participants' emotion awareness and is a novel visual communication pattern.

Collaborative graphics editing systems in ubiquitous environment support users to join and leave the collaboration dynamically at any time. The shared workspace state of these users will be inconsistent for the modification of the other session members. It is therefore necessary to initialize the application instance of the latecomer with the current state. In this paper, we propose a rejoining scheme that is based on the user interest prediction algorithm we proposed. The scheme is efficient for latecomers to rejoin the collaborative design session. The scheme is realized in our prototype system of collaborative graphics editing system that can work well in ubiquitous environments.

With heterogeneous devices proliferating, there is a need for collaborative graphics editing systems that are tailored to the capabilities of their current computing and communications environment. Mobile network and device capabilities constrain the application implementation, which implies that the storage space and computing power is limited and network is unreliable. This paper presents a semi-replicated architecture and an on demand consistency maintenance scheme for mobile sites to cooperate with other desktops efficiently. To maintain the consistency of the replicas on mobile embedded devices, either data consistency or semantic similarity is maintained based on the prediction of the regions of interest. The scheme is realized in our prototype system of collaborative graphics editing system that can work well in heterogeneous environments

Tracing the movement of telepointer is one of the most efficient ways to aware remote collaborators' drawing actions in real-time Internet based collaborative graphics editing systems. However, delay jitter may occur, which will make the presence of telepointer jumpy and lead to semantic misunderstanding or operation conflicting in collaboration. This paper presents a novel algorithm that predicts the moving track of telepointer based on historical movement cases. The algorithm is tested in our prototype system and is proved that it can limit the negative impact of jitter and provide collaborators with smooth and accurate telepointer motion awareness information

Locking is a common technique in distributed editing system used to prevent conflicting, maintain user intention and result consistency. Collaborative pattern design system is a special class, thus has special locking scheme. A novel scheme system forecast lock is proposed such that some regions are picked up by system where user wants to operate in the following steps, and these regions are locked then. System will settle conflict whenever they occurred. By using the scheme proposed, system locks regions to forward users' operations with great fluency, and prevent operating conflict.

The proliferation of computer devices and wireless networks allows users to access information and collaborative edit documents with others from anywhere and at anytime. Mobile network and device capabilities constrain the application implementation, which implies that the storage space and computing power is limited and network is unreliable. This paper presents a collaborative graphics editing system that can work well in mobile environment. To maintain the consistency of the replicas that dispersed on mobile sites, a semi-replicated architecture is proposed and the corresponding consistency schema is also presented. The schema is realized in MCES - a prototype system of collaborative graphics editing system that can work well in mobile environment.

For effective collaborative working between the parties in a pattern design project team, it is essential that a highly-efficient and feasible cooperative platform is available. This paper presents an agent-based infrastructure for the automated collaborative design of textile industrial patterns. Cooperative awareness among distributed participants is one of the most important issues associated with collaborative pattern design. Several techniques based on awareness intensity management are proposed. In addition, the Pattern Knowledge Library enables intelligent design on the World Wide Web and thus greatly enhances the functionality of computer-supported cooperative design (CSCD). This prototype is intended to serve as a useful cooperative system for designers and should allow faster, better and more economic collaborative design of patterns.