Software development is a collaborative process that involves various interactions among individuals and teams. TODO comments in source code play a critical role in managing and coordinating diverse tasks during this process. However, this study finds that a large proportion of open-source project TODO comments are left unresolved or take a long time to be resolved. About 46.7% of TODO comments in open-source repositories are of low-quality (e.g., TODOs that are ambiguous, lack information, or are useless to developers). This highlights the need for better TODO practices. In this study, we investigate four aspects regarding the quality of TODO comments in open-source projects: (1) the prevalence of low-quality TODO comments; (2) the key characteristics of high-quality TODO comments; (3) how are TODO comments of different quality managed in practice; and (4) the feasibility of automatically assessing TODO comment quality. Examining 2,863 TODO comments from Top100 GitHub Java repositories, we propose criteria to identify high-quality TODO comments and provide insights into their optimal composition. We discuss the lifecycle of TODO comments with varying quality. To assist developers, we construct deep learning-based methods that show promising performance in identifying the quality of TODO comments, potentially enhancing development efficiency and code quality.

Attracting and retaining newcomers are critical for the sustainable development of Open Source Software (OSS) projects. Considerable efforts have been made to help newcomers identify and overcome barriers in the onboarding process. However, fewer studies focus on newcomers’ activities before their successful onboarding. Given the rising popularity of deep learning (DL) techniques, we wonder what the onboarding process of DL newcomers is, and if there exist commonalities or differences in the onboarding process for DL and non-DL newcomers. Therefore, we reported a study to understand the growth trends of DL and non-DL newcomers, mine DL and non-DL newcomers’ activities before their successful onboarding (i.e., past activities), and explore the relationships between newcomers’ past activities and their first commit patterns and retention rates. By analyzing 20 DL projects with 9,191 contributors and 20 non-DL projects with 9,839 contributors, and conducting email surveys with contributors, we derived the following findings: 1) DL projects have attracted and retained more newcomers than non-DL projects. 2) Compared to non-DL newcomers, DL newcomers encounter more deployment, documentation, and version issues before their successful onboarding. 3) DL newcomers statistically require more time to successfully onboard compared to non-DL newcomers, and DL newcomers with more past activities (e.g., issues, issue comments, and watch) are prone to submit an intensive first commit (i.e., a commit with many source code and documentation files being modified). Based on the findings, we shed light on the onboarding process for DL and non-DL newcomers, highlight future research directions, and provide practical suggestions to newcomers, researchers, and projects.

Automated chromosome instance segmentation from metaphase cell microscopic images is critical for the diagnosis of chromosomal disorders (i.e., karyotype analysis). However, it is still a challenging task due to lacking of densely annotated datasets and the complicated morphologies of chromosomes, e.g., dense distribution, arbitrary orientations, and wide range of lengths. To facilitate the development of this area, we take a big step forward and manually construct a large-scale densely annotated dataset named AutoKary2022, which contains over 27,000 chromosome instances in 612 microscopic images from 50 patients. Specifically, each instance is annotated with a polygonal mask and a class label to assist in precise chromosome detection and segmentation. On top of it, we systematically investigate representative methods on this dataset and obtain a number of interesting findings, which helps us have a deeper understanding of the fundamental problems in chromosome instance segmentation. We hope this dataset could advance research towards medical understanding. The dataset can be available at:https://github.com/wangjuncongyu/chromosome-instance-segmentation-dataset.

Community search, which looks for query-dependent communities in a graph, is an important task in graph analysis. Existing community search studies address the problem by finding a densely-connected subgraph containing the query. However, many real-world networks are heterogeneous with rich semantics. Queries in heterogeneous networks generally involve in multiple communities with different semantic connections, while returning a single community with mixed semantics has limited applications. In this paper, we revisit the community search problem on heterogeneous networks and introduce a novel paradigm of heterogeneous community search and ranking. We propose to automatically discover the query semantics to enable the search of different semantic communities and develop a comprehensive community evaluation model to support the ranking of results. We build HeteroCS, a heterogeneous community search system with semantic explanation, upon our semantic community model, and deploy it on two real-world graphs. We present a demonstration case to illustrate the novelty and effectiveness of the system.

Reliable chromosome detection in metaphase cell (MC) images can greatly alleviate the workload of cytogeneticists for karyotype analysis and the diagnosis of chromosomal disorders. However, it is still an extremely challenging task due to the complicated characteristics of chromosomes, e.g., dense distributions, arbitrary orientations, and various morphologies. In this article, we propose a novel rotated-anchor-based detection framework, named DeepCHM, for fast and accurate chromosome detection in MC images. Our framework has three main innovations: 1) A deep saliency map representing chromosomal morphological features is learned end-to-end with semantic features. This not only enhances the feature representations for anchor classification and regression but also guides the anchor setting to significantly reduce redundant anchors. This accelerates the detection and improves the performance; 2) A hardness-aware loss weights the contribution of positive anchors, which effectively reinforces the model to identify hard chromosomes; 3) A model-driven sampling strategy addresses the anchor imbalance issue by adaptively selecting hard negative anchors for model training. In addition, a large-scale benchmark dataset with a total of 624 images and 27,763 chromosome instances was built for chromosome detection and segmentation. Extensive experimental results demonstrate that our method outperforms most state-of-the-art (SOTA) approaches and successfully handles chromosome detection, with an AP score of 93.53%.

SimRank is a popular link-based similarity measure on graphs. It enables a variety of applications with different modes of querying (e.g., single-pair, single-source and all-pair modes). In this paper, we propose UISim, a unified and incremental framework for all SimRank modes based on a scheduled approximation principle. UISim processes queries with incremental and prioritized exploration of the entire computation space, and thus allows flexible tradeoff of time and accuracy. On the other hand, it creates and shares common building blocks for online computation without relying on indexes, and thus is efficient to handle both static and dynamic graphs. Our experiments on various real-world graphs show that to achieve the same accuracy, UISim runs faster than its respective state-of-the-art baselines in each mode, and scales well on larger graphs.

The wide spread of fake news is increasingly threatening both individuals and society. Great efforts have been made for automatic fake news detection on asingledomain (e.g., politics). However, correlations exist commonly across multiple news domains, and thus it is promising to simultaneously detect fake news ofmultipledomains. Based on our analysis, we pose two challenges in multi-domain fake news detection: 1)domain shift, caused by the discrepancy among domains in terms of words, emotions, styles, etc. 2)domain labeling incompleteness, stemming from the real-world categorization that only outputs one single domain label, regardless of topic diversity of a news piece. In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M$^{3}$FEND) to address these two challenges. We model news pieces from a multi-view perspective, including semantics, emotion, and style. Specifically, we propose a Domain Memory Bank to enrich domain information which could discover potential domain labels based on seen news pieces and model domain characteristics. Then, with enriched domain information as input, a Domain Adapter could adaptively aggregate discriminative information from multiple views for news in various domains. Extensive offline experiments on English and Chinese datasets demonstrate the effectiveness of M$^{3}$FEND, and online tests verify its superiority in practice. Our code is available athttps://github.com/ICTMCG/M3FEND.

In this paper, we propose a novel elastic demand function that captures the price elasticity of demand in hotel occupancy prediction. We develop a price elasticity prediction model (PEM) with a competitive representation module and a multi-sequence fusion model to learn the dynamic price elasticity from a complex set of affecting factors. Moreover, a multi-task framework consisting of room- and hotel-level occupancy prediction tasks is introduced to PEM to alleviate the data sparsity issue. Extensive experiments on real-world datasets show that PEM outperforms other state-of-the-art methods for both occupancy prediction and dynamic pricing. PEM model has been successfully deployed at Fliggy and shown good performance in online hotel booking services.

SimRank is a popular link-based similarity measure on graphs. It enables a variety of applications with different modes of querying. In this paper, we propose UISim, a unified and incremental framework for all SimRank modes based on a scheduled approximation principle. UISim processes queries with incremental and prioritized exploration of the entire computation space, and thus allows flexible tradeoff of time and accuracy. On the other hand, it creates and shares common “building blocks” for online computation without relying on indexes, and thus is efficient to handle both static and dynamic graphs. Our experiments on various real-world graphs show that to achieve the same accuracy, UISim runs faster than its respective state-of-the-art baselines, and scales well on larger graphs.

Nowadays, vulnerabilities in open source software (OSS) are constantly emerging, posing a great threat to application security. Security patches are crucial in reducing the risk of OSS vulnerabilities. However, many of the vulnerabilities disclosed by CVE/NVD are not accompanied by security patches. Previous research has shown that the auxiliary information in CVE/NVD can aid in the matching of a vulnerability to appropriate commits. The state-of-art research proposed a rank-based approach based on the multiple dimensions of features extracted from the auxiliary information in CVE/NVD. However, this approach ignores the semantic features in the vulnerability descriptions and commit messages, making the model still have room for improvement. In this paper, we propose a novel ranking-based approach VCMATCH (Vulnerability-Commit Match). In addition to extracting the shallow statistical features between the vulnerability and the patch commit, VCMATCH extracts the deep semantic features of the vulnerability descriptions and commit messages. Besides, VCMATCH applies three classification models (i.e., XGBoost, LightGBM, CNN) and uses a voting-based rank fusion method to combine the results of the three models to generate a better result. We evaluate VCMATCH with 1,669 CVEs from 10 OSS projects. The experiment results show that VCMATCH can effectively identify security patches for OSS vulnerabilities in terms of Recall@K and Manual Effort@K, and outperforms the state-of-art model by a statistically significant margin.

Block-based programming is of great significance for cultivating children’s computational thinking. However, due to the following challenges, it is difficult to evaluate students’ programming ability in online learning systems: 1) compared with the traditional Online Judge (OJ) system, there is no standard answer for a given task in block-based programming; 2) in order to promote students’ interests, although the programs are not totally correct and unrelated to the task, the teacher will give a comparatively higher score. Therefore, current approaches involving output comparison and code analysis do not work effectively. Furthermore, deep learning methods also suffer from the problem of how to represent block code for classification. We propose a novel hybrid estimation model to address these challenges. We first learn graph embedding from the parsed Abstract Syntax Tree (AST) to present the logicality of the code. Next, we provide some methods to measure the workload and complexity of the code. Then, we extracted some key variables and task-irrelevant properties, introduced teacher bias. Finally, XGBoost was constructed for classification. Based on real-world data collected from an online Scratch platform by early-age students, our model outperforms KimCNN, ResNet-18, and Graph2Vec+XGBoost. Moreover, we provided statistical analyses and intuitive explanations to interpret the characteristics in various groups.

Programming screencasts have become a pervasive resource on the Internet, which help developers learn new programming technologies or skills. The source code in programming screencasts is an important and valuable information for developers. But the streaming nature of programming screencasts (i.e., a sequence of screen-captured images) limits the ways that developers can interact with the source code in the screencasts. Many studies use the Optical Character Recognition (OCR) technique to convert screen images (also referred to as video frames) into textual content, which can then be indexed and searched easily. However, noisy screen images significantly affect the quality of source code extracted by OCR, for example, no-code frames (e.g., PowerPoint slides, web pages of API specification), non-code regions (e.g., Package Explorer view, Console view), and noisy code regions with code in completion suggestion popups. Furthermore, due to the code characteristics (e.g., long compound identifiers like ItemListener), even professional OCR tools cannot extract source code without errors from screen images. The noisy OCRed source code will negatively affect the downstream applications, such as the effective search and navigation of the source code content in programming screencasts. In this article, we propose an approach named psc2code to denoise the process of extracting source code from programming screencasts. First, psc2code leverages the Convolutional Neural Network (CNN) based image classification to remove non-code and noisy-code frames. Then, psc2code performs edge detection and clustering-based image segmentation to detect sub-windows in a code frame, and based on the detected sub-windows, it identifies and crops the screen region that is most likely to be a code editor. Finally, psc2code calls the API of a professional OCR tool to extract source code from the cropped code regions and leverages the OCRed cross-frame information in the programming screencast and the statistical language model of a large corpus of source code to correct errors in the OCRed source code. We conduct an experiment on 1,142 programming screencasts from YouTube. We find that our CNN-based image classification technique can effectively remove the non-code and noisy-code frames, which achieves an F1-score of 0.95 on the valid code frames. We also find that psc2code can significantly improve the quality of the OCRed source code by truly correcting about half of incorrectly OCRed words. Based on the source code denoised by psc2code , we implement two applications: (1) a programming screencast search engine; (2) an interaction-enhanced programming screencast watching tool. Based on the source code extracted from the 1,142 collected programming screencasts, our experiments show that our programming screencast search engine achieves the precision@5, 10, and 20 of 0.93, 0.81, and 0.63, respectively. We also conduct a user study of our interaction-enhanced programming screencast watching tool with 10 participants. This user study shows that our interaction-enhanced watching tool can help participants learn the knowledge in the programming video more efficiently and effectively.

Human trajectory prediction is an essential task for various applications such as travel recommendation, location-sensitive advertisement, and traffic planning. Most existing approaches are sequential-model based and produce a prediction by mining behavior patterns. However, the effectiveness of pattern-based methods is not as good as expected in real-life conditions, such as data sparse or data missing. Moreover, due to the technical limitations of sensors or the traffic situation at the given time, people going to the same place may produce different trajectories. Even for people traveling along the same route, the observed transit records are not exactly the same. Therefore trajectories are always diverse, and extracting user intention from trajectories is difficult. In this paper, we propose an augmented-intention recurrent neural network (AI-RNN) model to predict locations in diverse trajectories. We first propose three strategies to generate graph structures to demonstrate travel context and then leverage graph convolutional networks to augment user travel intentions under graph view. Finally, we use gated recurrent units with augmented node vectors to predict human trajectories. We experiment with two representative real-life datasets and evaluate the performance of the proposed model by comparing its results with those of other state-of-the-art models. The results demonstrate that the AI-RNN model outperforms other methods in terms of top-k accuracy, especially in scenarios with low similarity.

Proximity search on heterogeneous graphs aims to measure the proximity between two nodes on a graph w.r.t. some semantic relation for ranking. Pioneer work often tries to measure such proximity by paths connecting the two nodes. However, paths as linear sequences have limited expressiveness for the complex network connections. In this paper, we explore a more expressive DAG (directed acyclic graph) data structure for modeling the connections between two nodes. Particularly, we are interested in learning a representation for the DAGs to encode the proximity between two nodes. We face two challenges to use DAGs, including how to efficiently generate DAGs and how to effectively learn DAG embedding for proximity search. We find distance-awareness as important for proximity search and the key to solve the above challenges. Thus we develop a novel Distance-aware DAG Embedding (D2AGE) model. We evaluate D2AGE on three benchmark data sets with six semantic relations, and we show that D2AGE outperforms the state-of-the-art baselines. We release the code on https://github.com/shuaiOKshuai.

Semantic proximity search on heterogeneous graph is an important task, and is useful for many applications. It aims to measure the proximity between two nodes on a heterogeneous graph w.r.t. some given semantic relation. Prior work often tries to measure the semantic proximity by paths connecting a query object and a target object. Despite the success of such path-based approaches, they often modeled the paths in a weakly coupled manner, which overlooked the rich interactions among paths. In this paper, we introduce a novel concept of interactive paths to model the inter-dependency among multiple paths between a query object and a target object. We then propose an Interactive Paths Embedding (IPE) model, which learns low-dimensional representations for the resulting interactive-paths structures for proximity estimation. We conduct experiments on seven relations with four different types of heterogeneous graphs, and show that our model outperforms the state-of-the-art baselines.

Semantic user search is an important task on heterogeneous social networks. Its core problem is to measure the proximity between two user objects in the network w.r.t. certain semantic user relation. State-of-the-art solutions often take a path-based approach, which uses the sequences of objects connecting a query user and a target user to measure their proximity. Despite their success, we assert that path as a low-order structure is insufficient to capture the rich semantics between two users. Therefore, in this paper we introduce a new concept of subgraph-augmented path for semantic user search. Specifically, we consider sampling a set of object paths from a query user to a target user; then in each object path, we replace the linear object sequence between its every two neighboring users with their shared subgraph instances. Such subgraph-augmented paths are expected to leverage both path»s distance awareness and subgraph»s high-order structure. As it is non-trivial to model such subgraph-augmented paths, we develop a Subgraph-augmented Path Embedding (SPE) framework to accomplish the task. We evaluate our solution on six semantic user relations in three real-world public data sets, and show that it outperforms the baselines.

Many real-world networks have a rich collection of objects. The semantics of these objects allows us to capture different classes of proximities, thus enabling an important task of semantic proximity search. As the core of semantic proximity search, we have to measure the proximity on a heterogeneous graph, whose nodes are various types of objects. Most of the existing methods rely on engineering features about the graph structure between two nodes to measure their proximity. With recent development on graph embedding, we see a good chance to avoid feature engineering for semantic proximity search. There is very little work on using graph embedding for semantic proximity search. We also observe that graph embedding methods typically focus on embedding nodes, which is an "indirect'' approach to learn the proximity. Thus, we introduce a new concept of proximity embedding, which directly embeds the network structure between two possibly distant nodes. We also design our proximity embedding, so as to flexibly support both symmetric and asymmetric proximities. Based on the proximity embedding, we can easily estimate the proximity score between two nodes and enable search on the graph. We evaluate our proximity embedding method on three real-world public data sets, and show it outperforms the state-of-the-art baselines.

The data exchange between GPGPUs and CPUs are becoming more and more important nowadays. One trend in industry to alleviate the long latency is to integrate CPUs and GPGPUs on a single chip. In this paper, we analyze the reference interactions between CPU and GPGPU applications with a CPU-GPGPU co-simulator that integrates the gem5 and gpgpu-sim together. Since the memory controllers are shared among all cores, we observe severe memory contention between them. The CPU applications suffer a 1.26x slowdown and 64.79% blocked time in main memory when they run parallels with GPGPU applications. To alleviate the contention and provide more memory band-width, shared last level caches (LLCs) are commonly employed in such systems. We test a banked shared LLC structure that implanted into the co-simulator. We show that a simple shared LLC contributes mostly to the GPGPU (2.13x to running alone and 1.7x to running in parallel), rather than CPU. With the help of LLC, the memory requests issued to main memory is reduced to 30.74%, the blocked time is reduced to 49.64%, which provides more memory bandwidth. The latency-sensitive CPU applications are suffered as the LLC buffer occupation is very high when they run with GPGPU in parallel. Besides, as the number of LLC cache bank grows, we reveal that CPU achieves higher speedup than GPGPUs by increasing LLC parallelism. Finally, we also discuss the impact of GPGPU L2 cache. And we find that fewer GPGPU L2 cache banks will lower the performance as they limits the parallelism of GPGPU. The observations and inferences in this paper may serve as a reference guide to future CPU-GPGPU shared LLC design.

The shared last-level cache (SLLC) in heterogeneous multicore system is an important memory component that shared and competitive between multiple cores, so how to improve the SLLC performance has become an important research area. Last-level cache (LLC) bypassing technique that bypasses the LLC a part of memory requests is one of the most effective methods. The bypassed requests are sent directly to off-chip main memory (DRAM) rather than eliminated. We find that the bypassed requests influence the original scheduling sequence in Memory Controller (MC) severely. Besides, immoderate bypassing will disturb the MC load balance. We propose a 3-step method memory that adjusts memory scheduling algorithm to optimize LLC bypassing performance. The first step is adding an independent bypass stream for bypassed requests. The second step is scheduling the bypass stream with a smaller probability than that of normal GPU stream. The third step is adding a guard mechanism for MC. By dynamically set and revoke the guard, we can avoid unbalanced bypassing. For case study, we applied the 3-step method on two modern memory schedulers. The experimental results show that after applied the 3-step method, the schedulers improve the system performance obviously.

Last-level caches (LLC) grow large with significant power consumption. As LLC's capacity increases, it becomes quite inefficient. As recent studies show, a large percent of cache blocks are dead during the cache time. There is a growing need for LLC management to reduce the number of dead block in the LLC. However, there is a significant power requirement for the dead block's in-placement and replacement operations. In this paper, we introduce a global priority table predictor, a technique which is used for determining a cache block's priority when it attempts to insert into the LLC. It is similar to previous predictors, such as reuse distance and dead block predictor. The global priority table is indexed by the hash value of the block address and stores the priority value of the associate cache block. The priority value can be used to drive a dead block replacement and bypass optimization. Through the priority table, a large number of dead blocks could be bypassed. It achieves an average reduction of 13.2% in the number of LLC miss for twenty single-thread workloads from the SPEC2006 suite and 29.9% for ten multi-programmed workloads. It also yields a geometric mean speedup of 8.6% for single-thread workloads and a geometric mean normalized weighted speedup of 39.1% for multi-programmed workloads.

FPGA dynamic partial reconfiguration (DPR) tend to be adopted for its flexibility and fewer resource consumption increasingly in hardware implementation, especially in communication devices. A crossbar scheduling algorithm is used to schedule the crossbar, or decide the order in which cells will be served. The is lip and FIRM are two classic crossbar scheduling algorithms, but they do not support DPR. Performance of these two algorithms differs under varying workloads (load: cells' arrival speed). With the development of DPR, implementations of these algorithms will have improvement both in performance and resource usage. DPR reduces 7.249% average delay than iSlip does and 0.013% average delay than FIRM does in 4×4 crossbar. It reduces 17.9% average delay than iSlip does and 0.039% average delay than FIRM does in 8×8 crossbar. In this paper, we compared the DPR implementation and non-DPR implementations (iSlip and FIRM) and found that the former reduces 37.744% LUTs and 47.874% FFs in 4×4 crossbar, and 47.325% LUTs and 49.907% FFs in 8×8 crossbar.

Most current semantic Web services (SWS) discovery approaches focus on the matchmaking of services in a specific description language while in practical application the advertised services are often heterogeneous and distributed. This paper proposes a metric space approach to resolve this problem in which all heterogeneous Web services are modeled as metric objects regardless of concrete description languages, and thereby the discovery problem can be treated as similarity search in metric space with a uniform criterion. In the matchmaking process, both the functional semantics and non-functional semantics of the Web services are integrated as selection conditions for similarity query. And two types of similarity queries: range query and an improved nearest neighbor query are combined to produce a sorted result set.

As adaptive Web applications are gaining importance in software domains nowadays, there is a need for an effective method to construct such applications. In this paper, we propose MAWA, a method for model-driven development of adaptive Web applications. MAWA considers the architecture of adaptive Web applications and the key elements of the development activity. It adopts an iterative, incremental development process, and the adaptive model, which is composed of context model and user model, is highlighted. Besides, the adaptive categories and mechanism are specified in MAWA, which are used to support the implementation of the adaptive behaviors. A code generation strategy is integrated into MAWA, and it can help us to produce the applications quickly and effectively.

An original GA (genetic algorithm) is usually used for QoS (quality of service)-based Web services selection, however, such algorithm has to do a large redundancy repeat to find a solution for the reason that it does not make use of adequate output information. Thus, the efficiency and precision of GA are reduced. Moreover, fixed fitness function can not change to fit for different composite situations and complete automatic process so GA sometimes would miss the most suitable solution. To remedy this situation, this paper proposes a novel algorithm GBAA (genetic based ant algorithm) which could put feedback information to original GA by using MMAS (max-min ant system) and it also overcome some drawbacks of MMAS such as long time need and slow convergence. The new approach could gain the benefits of both GA and MMAS. Besides GBAA DC (divide and composite) method and rank sort method are added. DC is adopted to analysis business requirement and to build process. After computing QoS attributes, a manual step is allowed to put into selection process and a rank sort method is used to distinguish profits of different solutions. The automatic process which is cooperated with manual process could help user to make more effective decisions on Web service selection.

Web services and SOA technologies are growing with a fast rate but still facing many problems due to their heterogeneous nature. This paper, based on OWL-S, presents a rich and extensible ontology framework named OWL-QSP for service compositions. In the framework, Service Type is imported to improve service abstract level, and QoS, situation, context are adopted. Since service discovery, service selection and service execution can adapt to the changing situation, QoS and situation-aware service-based systems are more dynamic and flexible so to better satisfy the users' functional and non-functional requirements. The introducing of policy permits managing WSs at a high level and facilitate reuse. It also presents SMICE, a prototype of the service composition system, and describes its main components with service composition process.

This paper presents a framework for automated software development: Xauto, which includes four key elements: layer language, template, framework and component. Based on software system patterns, an automatic development process is supported and realized by template engineering and the mapping of layer languages. The authors expound the mapping patterns of Xauto framework in the three aspects of model, view and controller, and demonstrate them by the relevant template examples. The research on Xauto framework solidifies the mature solutions dealing with problems in certain domains and makes it reusable, and facilitates the change of software development mode from personal workshop to template engineering. This shift, therefore, will promote the automatic degree of software development and make it more efficient

Software development is considered to be a kind of collaborative activity now. In this paper, we first analyze the software development activity using activity theory, and highlight its collaborative features. We then propose an architecture of process-centered context-aware software development environment, CASDE, which fully considers the key elements of PCSDEs, especially the context element. The supportive and integrated nature of the environment is emphasized in CASDE. As illustrated in activity theory, the architecture can support the three levels of collaboration, i.e., co-ordinated, co-operative, and co-constructive level. In particular, the co-operative level is supported sufficiently by the introduction of context model and context process mechanism. Based on the architecture, the software development activity can be more collaborative and quality of software system can be improved

COTS (commercial off the shelf) component products undergo a technology refresh and renewal cycle. New versions or releases of COTS component products are brought to the market frequently. And rapid evolution of the COTS component product means the obsolescence of the COTS-based systems, especially during COTS-based systems' maintenance (in a long run). This is the fundamental problem to application developer. So the evaluation of the system's COTS component part is primary issue. This paper provides an open evaluation model to complete the task. The model bases on AHP and WSM, considering of COTS components lifecycle

This paper presents UPME, a metamodel approach to software process modeling based on UML extension. In this approach, software process modeling will take three steps: firstly, a metamodel modeling step to build metamodels for software process domain; secondly, a model instantiation step to build the model for a concrete project based on domain metamodels; and thirdly, a model compilation step to translate the model into object-oriented code skeleton for process enactment. In these steps, UML and its extension mechanisms are used, and an instantiation description language is designed to write process instantiation scripts. We built the ISPW-6 process as example, and the result demonstrated that the UML based metamodel approach makes process modeling more reusable and easier. And the popularity of UML also makes this approach more acceptable in industry than other specialized process modeling languages (PMLs).

It is evident that a STEP-based PnP (plug-and-play) CAD/CAPP integrative tool is very helpful for product data integration. However, the problem is in the difficulty in developing an integrated PnP tool that can be applied to heterogeneous CAD and CAPP systems. This paper presents the framework of a CAD/CAPP integrative tool with PnP characteristics. This implementation is by a series of model mapping from AP203 to AP224 and between AP214 and AP203/AP224.

Collaborative virtual environment model and role scheme are two research branches in computer science. Collaborative activities need an effective mechanism to support participants' roles and rights 'while role schemes can be applied to this field. In this paper, we present a new generic role-based and agent-oriented model for collaborative virtual environment based on discussion of collaborative activity as an essential role in the research of CVE. It introduces role schemes to establish more efficient collaborative virtual environments. The proposed model includes two important parts: collaborative entity and collaborative event. It also advances intelligent entity in running state and collaborative federation in this model to describe cooperation in collaborative virtual environment.