Cyber-Argumentation platforms facilitate large-scale online deliberations with appropriate structures and features to capture decision rationale and collective intelligence. Research advances have been made on its model, algorithms, and applications. Social networking has significant impacts on the formation and evolution of individual and collective opinions in cyber-argumentation. Social connections in social networking aspects like friends and follows relationships among users in cyber argumentation can reveal the patterns, shifts, and true relationships between the users in a discourse that influence collective decision-making process. However, the impacts of social networks on the formation and evolution of collective opinions in cyber-argumentation have not been studied systematically. There are currently no methods for assessing the impacts of social networks on the formation of collective opinions in cyber-argumentation. In this research, we propose a method to quantify the impact of social networking on cyber-argumentation. We investigate three types of impacts: directional impact, mutual impact, and social group impact. These represents the contribution of an individual user or group to the formation of a collective opinion for a specific issue in intelligent cyber argumentation system, a platform for cyber argumentation with social networking we have developed. These different measures help quantify what a person's social group thinks about his or her opinions and their impacts on their arguments. Finally, using these impact measures, we developed an algorithm to detect the pseudo-supporters of an individual in an argumentation network. The proposed methods and algorithm have been implemented based on our intelligent cyber-argumentation system with social networking. They have been studied empirically which shows that our quantification methods and algorithm enable the quantitative assessment of social connections and the detection of pseudo-supporters in cyber-argumentation.

Summary Online argumentation systems enable stakeholders to post their problems under consideration and solution alternatives and to exchange arguments over the alternatives posted in an argumentation tree. In an argumentation process, stakeholders have their own opinions, which very often contrast and conflict with opinions of others. Some of these opinions may be outliers with respect to the mean group opinion. This paper presents a method for identifying stakeholders with outlier opinions in an argumentation process. It detects outlier opinions on the basis of individual stakeholder's opinions, as well as collective opinions on them from other stakeholders. Decision makers and other participants in an argumentation process therefore have an opportunity to explore the outlier opinions within their groups from both individual and group perspectives. In a large argumentation tree, it is often difficult to identify stakeholders with outlier opinions manually. The system presented in this paper identifies them automatically. Experiments are presented to evaluate the proposed method. Their results show that the method detects outlier opinions in an online argumentation process effectively.

The rapid growth in the number and diversity of Web APIs, coupled with the myriad of functionally similar Web APIs, makes it difficult to find most suitable Web APIs for users to accelerate and accomplish Mashup development. Even if the existing methods show improvements in Web APIs recommendation, it is still challenging to recommend Web APIs with high accuracy and good diversity. In this paper, we propose an integrated content and network-based service clustering and Web APIs recommendation method for Mashup development. This method, first develop a two-level topic model by using the relationship among Mashup services to mine the latent useful and novel topics for better service clustering accuracy. Moreover, based on the clustering results of Mashups, it designs a collaborative filtering (CF) based Web APIs recommendation algorithm. This algorithm, exploits the implicit co-invocation relationship between Web APIs inferred from the historical invocation history between Mashups clusters and the corresponding Web APIs, to recommend diverse Web APIs for each Mashups clusters. The method is expected to not only find much better matched Mashups with high accuracy, but also diversify the recommendation result of Web APIs with full coverage. Finally, based on a real-world dataset from ProgrammableWeb, we conduct a comprehensive evaluation to measure the performance of our method. Compared with existing methods, experimental results show that our method significantly improves the accuracy and diversity of recommendation results in terms of precision, recall, purity, entropy, DCG and HMD.

Existing systems allow manufacturers to acquire factory floor data and perform analysis with cloud applications for machine health monitoring, product quality prediction, fault diagnosis and prognosis etc. However, they do not provide capabilities to perform testing of machine tools and associated components remotely, which is often crucial to identify causes of failure. This paper presents a fault diagnosis system in a cyber-physical manufacturing cloud (CPMC) that allows manufacturers to perform diagnosis and maintenance of manufacturing machine tools through remote monitoring and online testing using Machine Tool Communication (MTComm). MTComm is an Internet scale communication method that enables both monitoring and operation of heterogeneous machine tools through RESTful web services over the Internet. It allows manufacturers to perform testing operations from cloud applications at both machine and component level for regular maintenance and fault diagnosis. This paper describes different components of the system and their functionalities in CPMC and techniques used for anomaly detection and remote online testing using MTComm. It also presents the development of a prototype of the proposed system in a CPMC testbed. Experiments were conducted to evaluate its performance to diagnose faults and test machine tools remotely during various manufacturing scenarios. The results demonstrated excellent feasibility to detect anomaly during manufacturing operations and perform testing operations remotely from cloud applications using MTComm.

With the broad adoption of service-oriented architecture, many software systems have been developed by composing loosely-coupled Web services. Service discovery, a critical step of building service-based systems (SBSs), aims to find a set of candidate services for each functional task to be performed by an SBS. The keyword-based search technology adopted by existing service registries is insufficient to retrieve semantically similar services for queries. Although many semantics-aware service discovery approaches have been proposed, they are hard to apply in practice due to the difficulties in ontology construction and semantic annotation. This paper aims to help service requesters (e.g., SBS designers) obtain relevant services accurately with a keyword query by exploiting domain knowledge about service functionalities (i.e., service goals) mined from textual descriptions of services. We firstly extract service goals from services’ textual descriptions using an NLP-based method and cluster service goals by measuring their semantic similarities. A query expansion approach is then proposed to help service requesters refine initial queries by recommending similar service goals. Finally, we develop a hybrid service discovery approach by integrating goal-based matching with two practical approaches: keyword-based and topic model-based. Experiments conducted on a real-world dataset show the effectiveness of our approach.

The cloud manufacturing yields insights of manufacturing services over cyber space based on integration of advanced manufacturing with cloud computing. However, the different communication standards between the different system levels are main challenges for the integration of them without a conflicting communication. Ethernet appears as the best solution to support all levels for industrial manufacturing systems. Since the synchronous integration is fulfilled, the prerequisite for deciding the overall performance of systems is adaptable real-time communication. This motivates us to study the problem of designing a new real-time communication based on Ethernet standard to enable real-time processing for factory networks, termed Real-Time Ethernet (RTEthernet). The objective is to schedule maximum number of machines transmitting data through a controller in a data transmission window (Coordinated Cycle) as well as guarantee the real-time communication. The problem is called the Maximum Number of Real-time Data Transmission (MNRDT) problem. Two relevant solutions are proposed for addressing the MNRDT problem. Simulation results are provided to show the performance of proposed solutions.

The rapid growth in the number and diversity of Mashup services, coupled with the myriad of functionally similar Mashup services, makes it difficult to find suitable Mashup services to develop Mashup-based software applications due to an unprecedentedly large number of choices of Mashup services. Even if the existing latent factor based methods show significant improvements in Mashup service clustering and discovery, it is still challenging to find Mashup services with high accuracy due to overlooking of relationships among Mashup services. The relationships among Mashup services actually can be exploited in mining latent functional factors to improve the accuracy of clustering and discovery. In this paper, we propose a Mashup service clustering method based on an integration of service content and network via exploiting a two-level topic model. This method, firstly designs a two-level topic model to mine latent topics for representing functional features of Mashup services. Secondly, it uses two different random walk processes to derive and incorporate the topic distribution of Mashup services at service network level into the topic distribution of Mashup services at the service content level. Thirdly, K-means and Agnes algorithm are used to perform Mashup service clustering based on latent topics' similarity. Finally, we conduct a comprehensive evaluation to measure performance of our method. Compared with other existing clustering approaches, experimental results show that our approach achieves a significant improvement in terms of precision, recall, purity and entropy.

The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With the increasing adoption and presence of web services, design of novel approaches for effective web service recommendation to satisfy users’ potential requirements has become of paramount importance. Existing web service recommendation approaches mainly focus on predicting missing QoS values of web service candidates which are interesting to a user using collaborative filtering approach, content-based approach, or their hybrid. These recommendation approaches assume that recommended web services are independent to each other, which sometimes may not be true. As a result, many similar or redundant web services may exist in a recommendation list. In this paper, we propose a novel web service recommendation approach incorporating a user's potential QoS preferences and diversity feature of user interests on web services. User's interests and QoS preferences on web services are first mined by exploring the web service usage history. Then we compute scores of web service candidates by measuring their relevance with historical and potential user interests, and their QoS utility. We also construct a web service graph based on the functional similarity between web services. Finally, we present an innovative diversity-aware web service ranking algorithm to rank the web service candidates based on their scores, and diversity degrees derived from the web service graph. Extensive experiments are conducted based on a real world web service dataset, indicating that our proposed web service recommendation approach significantly improves the quality of the recommendation results compared with existing methods.

Collaborative Filtering (CF) is widely employed for making Web service recommendation. CF-based Web service recommendation aims to predict missing QoS (Quality-of-Service) values of Web services. Although several CF-based Web service QoS prediction methods have been proposed in recent years, the performance still needs significant improvement. First, existing QoS prediction methods seldom consider personalized influence of users and services when measuring the similarity between users and between services. Second, Web service QoS factors, such as response time and throughput, usually depends on the locations of Web services and users. However, existing Web service QoS prediction methods seldom took this observation into consideration. In this paper, we propose a location-aware personalized CF method for Web service recommendation. The proposed method leverages both locations of users and Web services when selecting similar neighbors for the target user or service. The method also includes an enhanced similarity measurement for users and Web services, by taking into account the personalized influence of them. To evaluate the performance of our proposed method, we conduct a set of comprehensive experiments using a real-world Web service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.

Existing service recommendation methods, that employ memory-based collaborative filtering (CF) techniques, compute the similarity between users or items using nonfunctional attribute values obtained at service invocation. However, using these nonfunctional attribute values from invoked services alone in similarity computation for personalized service recommendation is not sufficient. This is because two users may invoke the same service, but their personalized preferences on nonfunctional attributes that describe the service may be different. Thus, to accurately personalize service recommendation, it is necessary for CF-based recommendation systems to incorporate users personalized preferences on nonfunctional attributes when recommending services to an active user. This paper proposes a CF-based service recommendation method that considers users' personalized preference on nonfunctional attributes. We first compute the satisfaction of an active user's preference on nonfunctional attribute(s) and then use these satisfaction values to obtain their similarity measures. We then employ the top-k algorithm to identify neighbors of the active user and subsequently, use the weighted average with mean offset method to predict his/her nonfunctional attribute. We evaluate our method using real-world services and also conduct experiments to show that the proposed method improves recommendation accuracy significantly.

The growing number of Additive Manufacturing Web (AMW) services, offered by different providers over the Internet, makes it challenging for consumers to compare these AMW services to select a service of their choice. In addition, it is even more challenging for consumers to compare these AMW services against their personal preferences. This is because, consumers personal preferences on multiple non-functional attributes such as price, material, accuracy and schedule, should be considered for AMW service selection. The decentralized nature of AMW services coupled by the need to consider consumers personal preferences during AMW service selection, requires a system that will serve as a broker between AMW services and consumers. In this paper, we propose a service broker system for AMW services that provides consumers with a single point of access to a large number of AMW services from many additive manufacturing service providers. This broker system also incorporates the first real application of service selection with fuzzy logic based personalized preferences and trade-off. We develop a method to generate fuzzy membership functions for each non-functional attribute. This makes it easy for consumers to specify their fuzzy membership functions. Finally, we present an application case study to demonstrate the feasibility of brokerage in AMW services and also evaluate our method in terms of performance.

For service users to get the best service that meet their requirements, they prefer to personalize their nonfunctional attributes, such as reliability and price. However, the personalization makes it challenging for service providers to completely meet users’ preferences, because they have to deal with conflicting nonfunctional attributes when selecting services for users. With this in mind, users may sometimes want to explicitly specify their trade-offs among nonfunctional attributes to make their preferences known to service providers. In this article, we present a novel service selection method based on fuzzy logic that considers users’ personalized preferences and their trade-offs on nonfunctional attributes during service selection. The method allows users to represent their elastic nonfunctional requirements and associated importance using linguistic terms to specify their personalized trade-off strategies. We present examples showing how the service selection framework is used and a prototype with real-world airline services to evaluate the proposed framework's application.

Recommending trusted services to users is of paramount value in service-oriented environments. Reputation has been widely used to measure the trustworthiness of services, and various reputation models for service recommendation have been proposed. Reputation is basically a global trust score obtained by aggregating trust from a community of users, which could be conflicting with an individual's personal opinion on the service. Evaluating a service's trustworthiness locally based on the evaluating user's own or his/her friends' experiences is sometimes more accurate. However, local trust assessment may fail to work when no trust path from an evaluating user to a target service exists. This paper proposes a hybrid trust-aware service recommendation method for service-oriented environment with social networks via combining global trust and local trust evaluation. A global trust metric and a local trust metric are firstly presented, and then a strategy for combining them to predict the final trust of service is proposed. To evaluate the proposed method's performance, we conducted several simulations based on a synthesized dataset. The simulation results show that our proposed method outperforms the other methods in service recommendation.

Intelligent argumentation system facilitates stakeholders to exchange dialogue over issues and provides decision support by capturing rationale of the stakeholders through arguments. In argumentation process, stakeholders tend to polarize on their opinions and form polarization groups. A method [1] was developed earlier to identify polarization groups, however, polarization groups tend to overlap to a certain degree and each stakeholder may be a member of multiple polarization groups to varied degrees. Quantifying stakeholders' membership in multiple polarization groups in argumentation for collaborative decision making is not addressed earlier. We present an approach using fuzzy clustering algorithm to address this issue and evaluate the approach using an argumentation tree built by twenty four stakeholders.

This paper presents the first computer aided software analysis tool based on HOOMT (High Order Object-oriented Modeling Technique), which provides facilities for structured object-oriented analysis by integrating structured analysis and object-oriented analysis. It contains a graphical user interface for HOOMT-based modeling, supports modeling information management, and provides functionalities for developing three component sub-models in HOOMT: High Order Object Model (HOOM), Hierarchical Object Information Flow Model (HOIFM), and Hierarchical State Transition Model (HSTM). It supports structured decomposition of high-order objects, processes, and states. We describe its system architecture and implementation in this paper.

Perception of trustworthiness of service providers is a fundamental need in service selection. Trust propagation has been used to predict trustworthiness of service providers in service-oriented social networks. However, existing trust propagation methods may suffer from a scalability problem, i.e., their computation time is likely too high to be acceptable in practice, especially when they are applied to very large-scale service-oriented social networks. Moreover, they rarely consider the structural properties of social networks to optimize their performance. This paper proposes an efficient trust propagation scheme for predicting trust in service-oriented social networks. It exploits the specific structural properties of social networks and builds an advanced data structure from preprocessing to improve the efficiency of trust propagation. Our scheme can support multiple trust propagation strategies. Experiments show that our scheme is much more efficient than well-known trust propagation methods in trust prediction, while its trust prediction results are as accurate as theirs in service-oriented social networks.

Ensuring that the non-functional requirements (NFRs), of a system are satisfied is an essential task in software development. However, this task is complicated by the fact that many NFRs conflict with each other from multiple perspectives. It is essential to resolve conflicts collectively in a collaborative decision making process since stakeholders often disagree on how conflicts should be resolved. In this paper, we describe a method for dividing high-level NFR conflicts within a product line into more manageable sub-problems. Stakeholders make use of an argumentation based collaborative decision support system to determine which design alternatives provide the best trade-offs between NFRs. Finally, we present an empirical study in which the aforementioned system was used to resolve a single instance of an NFR conflict across 3 members of a product line. It shows that the system is effective in resolving conflicts in a collaborative decision process.

Web services are very prevalent nowadays. Recommending Web services that users are interested in becomes an interesting and challenging research problem. In this paper, we present AWSR (Active Web Service Recommendation), an effective Web service recommendation system based on users' usage history to actively recommend Web services to users. AWSR extracts user's functional interests and QoS preferences from his/her usage history. Similarity between user's functional interests and a candidate Web service is calculated first. A hybrid new metric of similarity is developed to combine functional similarity measurement and nonfunctional similarity measurement based on comprehensive QoS of Web services. The AWSR ranks publicly available Web services based on values of the hybrid metric of similarity, so that a Top-K Web service recommendation list is created for a user. AWSR has been implemented and deployed on the Web. By conducting large-scale experiments based on a real-world Web services dataset, it is shown that our system effectively recommends Web services based on users functional interests and non-functional requirements with excellent performance.

Collaborative filtering is one of widely used Web service recommendation techniques. In QoS-based Web service recommendation, predicting missing QoS values of services is often required. There have been several methods of Web service recommendation based on collaborative filtering, but seldom have they considered locations of both users and services in predicting QoS values of Web services. Actually, locations of users or services do have remarkable impacts on values of QoS factors, such as response time, throughput, and reliability. In this paper, we propose a method of location-aware collaborative filtering to recommend Web services to users by incorporating locations of both users and services. Different from existing user-based collaborative filtering for finding similar users for a target user, instead of searching entire set of users, we concentrate on users physically near to the target user. Similarly, we also modify existing service similarity measurement of collaborative filtering by employing service location information. After finding similar users and services, we use the similarity measurement to predict missing QoS values based on a hybrid collaborative filtering technique. Web service candidates with the top QoS values are recommended to users. To validate our method, we conduct series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that the location-aware method improves performance of recommendation significantly.

Trustworthiness plays an important role in service selection and usage. However, it is not easy to define and compute the service trustworthiness because of its subject meaning and also the different views on it. In this paper, we describe the meaning of trustworthiness and the computation method for trustworthiness fusion. Through extracting trustworthiness from service provider, service requestor and service broker, we creatively adopted D-S (Dempster-Shafer) evident theory to fuse the tripartite trustworthiness. Finally, we completed some comparison experiments on our web service supermarket platform and certified the efficiency of our method.

Users usually have different prospective even they input a same keyword to search Web services. It is a challenge to personalize web service search engine as more and more keyword-like Web services becoming available on Internet. User interest plays an important role in personalizing search result. Therefore, through interest extraction, Web service search engine is personalized. At last, an experiment is presented to demonstrate the feasibility of the method.

For services that have similar functionalities, if they are published by different cloud platforms, it is a challenge to evaluate them, for satisfying different end users' personal preferences. In view of this challenge, a method for web service ranking, named WSRank, is investigated in cloud environment in this paper. It aims at ranking different Web services published by different cloud platforms, taking advantage of Page Rank principle. At last, a case study and experiment are presented to demonstrate the feasibility of the method.

Collaborative filtering is one of widely used Web service recommendation techniques. There have been several methods of Web service selection and recommendation based on collaborative filtering, but seldom have they considered personalized influence of users and services. In this paper, we present an effective personalized collaborative filtering method for Web service recommendation. A key component of Web service recommendation techniques is computation of similarity measurement of Web services. Different from the Pearson Correlation Coefficient (PCC) similarity measurement, we take into account the personalized influence of services when computing similarity measurement between users and personalized influence of services. Based on the similarity measurement model of Web services, we develop an effective Personalized Hybrid Collaborative Filtering (PHCF) technique by integrating personalized user-based algorithm and personalized item-based algorithm. We conduct series of experiments based on real Web service QoS dataset WSRec [11] which contains more than 1.5 millions test results of 150 service users in different countries on 100 publicly available Web services located all over the world. Experimental results show that the method improves accuracy of recommendation of Web services significantly.

Web service selection based on quality of service (QoS) has been a research focus in an environment where many similar web services exist. Current methods of service selection usually focus on a single service request at a time and the selection of a service with the best QoS at the user's own discretion. The selection does not consider multiple requests for the same functional web services. Usually, there are multiple service requests for the same functional web service in practice. In such situations, conflicts occur when too many requesters select the same best web service. This paper aims at solving these conflicts and developing a global optimal service selection method for multiple related service requesters, thereby optimizing service resources and improving performance of the system. It uses Euclidean distance with weights to measure degree of matching of services based on QoS. A 0-1 integral programming model for maximizing the sum of matching degree is created and consequently, a global optimal service selection algorithm is developed. The model, together with a universal and feasible optimal service selection algorithm, is implemented for global optimal service selection for multiple requesters (GOSSMR). Furthermore, to enhance its efficiency, Skyline GOSSMR is proposed. Time complexity of the algorithms is analyzed. We evaluate performance of the algorithms and the system through simulations. The simulation results demonstrate that they are more effective than existing ones.

Technical requirements are hard to determine in software development. They are often specified subjectively in practice. Poorly determined technical requirements often lead to poor customer satisfaction, cost overrun, and delay in schedule, and poor quality. Quality Function Deployment (QFD) is one of major engineering methods used to elicit customer's needs and transforms them into technical requirements in industry. It has been applied to develop numerous products, including software systems, to improve their quality. However, target setting for technical software requirements is a complicated and challenging task in product development in Software Quality Function Deployment (SQFD). Current methods for target setting for technical software requirements do not consider their technical trends for a given timeframe. As a result, by the time of completion of a project the target values of technical requirements may not be competitive any more. In this paper, we first discuss benchmarking, primitive linear regression and target setting based on impact analysis to set targets for technical requirements in Software Quality Function Deployment (SQFD). We then develop a method of target setting for technical requirements by incorporating timeframe and the technical trend. It can help us to assess impact of both under-achieved and over-achieved targets. By incorporating the technical trend and the time of delivery of the product into target setting process, we can set targets for technical requirements that provide a competitive edge for our product over the competitor's products and a high level of customer satisfaction.

There are at least two challenges with quality management of service-oriented architecture based web service systems: 1) how to link its technical capabilities with customer's needs explicitly to satisfy customers' functional and nonfunctional requirements; and 2) how to determine targets of web service design attributes. Currently, the first issue is not addressed and the second one is dealt with subjectively. Quality Function Deployment (QFD), a quality management system, has found its success in improving quality of complex products although it has not been used for developing web service systems. In this paper, we analyze requirements for web services and their design attributes, and apply the QFD for developing web service systems by linking quality of service requirements to web service design attributes. A new method for technical target setting in QFD, based on an artificial neural network, is also presented. Compared with the conventional methods for technical target setting in QFD, such as benchmarking and the linear regression method, which fail to incorporate nonlinear relationships between design attributes and quality of service requirements, it sets up technical targets consistent with relationships between quality of web service requirements and design attributes, no matter whether they are linear or nonlinear.

The primary goal of this paper is to develop a structured object-oriented security requirements analysis methodology for the elicitation and analysis of security requirements in embedded systems. There are several approaches to elicit, analyze and specify security requirements in embedded systems ranging from formal mathematical models for proof of certain security properties to informal methods that are easily understood. Applicability of formal security models is limited because they are complex and it is time consuming to develop. On the other hand, informal security requirements analysis methods are not integrated with conceptual models in requirements analysis, and although both external and internal threats have been dealt using use cases and misuse cases, they provide no process for analyzing both internal and external threats in a structured manner. This paper discusses a structured object-oriented security requirements analysis methodology for the elicitation and analysis of security requirements in embedded systems. It is capable of identifying hierarchically both external and internal threats posed by both external and internal actors of a system level by level. It is illustrated and validated by security requirements analysis for an advanced embedded power grid control system.

Conflicts among non-functional requirements are often identified subjectively and there is a lack of conflict analysis in practice. Current approaches fail to capture the nature of conflicts among non-functional requirements, which makes the task of conflict resolution difficult. In this paper, a framework has been provided for the analysis of conflicts among non-functional requirements using the integrated analysis of functional and non-functional requirements. The framework identifies and analyzes conflicts based on relationships among quality attributes, functionalities and constraints. Since, poorly structured requirement statements usually result in confusing specifications; we also developed canonical forms for representing non-functional requirements. . The output of our framework is a conflict hierarchy that refines conflicts among non-functional requirements level by level. Finally, a case study is provided in which the proposed framework was applied to analyze and detect conflicts among the non-functional requirements of a search engine.

Potential software failures present a sizable risk element in the design and development of many systems. In this paper, we augment the Software Function-Failure Design method, which is capable of predicting potential software failures in the very early stages of design, with the Risk in Early Design technique. This synergistic combination allows a risk assessment to be conducted at an early time in the software development process when traditional techniques are not applicable. The results are concise risk statements regarding the potential failure of functionalities with likelihood and consequence quantifications that can be used as part of a risk management program. The process is illustrated using a software failure database for the NASA Mars Exploratory Rover.

The current object-oriented class-based approaches to hardware/software co-analysis/co-design of embedded systems are limited in their abilities to properly capture the structure of individual instances of hardware and software components and their interactions. This paper discusses a methodology to extend a structured object-oriented hardware/software co-design methodology based on the high order object-oriented modeling technique (HOOMT) to incorporate instance-based object and behavioral models. The instance-based structured object-oriented methodology will enable description of a system's structure based on individual instances of hardware and software components and specification of the interactions among them. In addition, lattices are introduced to specify the concurrent behavior of hardware/software components in a concurrent embedded system. These additions further enhance the method's capability of providing a precise set of specifications that can be understood easily by both hardware and software designers in co-analysis/co-design. The methodology has been applied to the specification of hardware and software of a simulated advanced power grid control system

The hardware/software co-design of a high-quality real-time system relies heavily on the modeling of both the hardware and software components from three aspects: structures, functionalities, and constraints, especially the temporal constraints. However, there is not a systematic process for the elicitation and analysis of temporal constraints in hardware/software co-design. Furthermore, existing object-oriented methods provide no means for the explicit specification of system/component constraints in object models. This paper presents a systematic methodology for structured object-oriented analysis and specification of temporal constraints in hardware/software co-analysis and co-design using an extended high-order object-oriented modeling technique (HOOMT). This methodology hierarchically elicits and analyzes the temporal constraints in hardware/software co-design based on the integration of the high-order object model (HOOM) and hierarchical timed automata (HTA). It helps to identify temporal constraints of hardware and software components as well as their interactions level by level. In addition, it helps trace the relationships among these constraints at multiple levels during the co-design of real-time systems. A case study from the hardware/software co-design of the simulated FACTS power transmission system is used to illustrate the feasibility and merits of this methodology

In this position paper, challenges and issues with collaborative global software development and education are discussed. Suggestions are made to improve and strengthen software engineering education to adapt to it.

There are several approaches to the hardware/software design in embedded systems, ranging from the traditional sequential methods which focus on the determination of the hardware architecture prior to software design, to newer object-oriented approaches that attempt to apply software engineering methods to hardware design without a systematic process. This paper discusses a structured object-oriented methodology for the integrated co-analysis and co-design of hardware/software systems using an extended high order object-oriented modeling technique (HOOMT). This methodology offers a uniform method for hardware and software developers to jointly develop the specifications for and partitioning of the hardware and software components of a system, as well as developing the interfaces between components, and allows easy design migration of components between hardware and software. In this paper it is applied to the co-analysis/co-design of the hardware and software of a simulated advanced power grid control system.

The development of complex software systems involves collecting software requirements from various stakeholders. Often stakeholder perceptions conflict during the requirements elicitation phase. An effective technique to resolve such a conflict is needed. We presented a framework that prioritizes software requirements gathered from multiple stakeholders by incorporating inter-perspective relationships, which is not addressed by existing priority assessment techniques. We use a relationship matrix to analyze the impact between requirements and facilitate the integration process which assesses their priorities based on their relationships from multiple perspectives. It allows the development team to resolve conflicts effectively and concentrate their valuable time and resources on the critical few requirements from multiple perspectives that directly contribute to high customer satisfaction.

The integration of object oriented modeling and structured analysis (SA) for developing a well structured object oriented software system is a challenge for requirements analysts and software designers. Some of the existing object oriented modeling techniques adopt approaches that are very different from SA, and others have clumsily stayed with SA after a few modifications. Our High Order Object Modeling Technique (HOOMT) however, attempts to strike a mean between both extremes while at the same time provides an effective modeling method. HOOMT consists of two models, the High Order Object Model and the Object Information Flow Model. By using the familiar divide and conquer concept and functional decomposition, our approach also ensures a less stressful migration of SA analysts to object oriented platforms. The paper introduces HOOMT, its concepts, and notations.

A framework for a process planning system for the machining of axisymmetric parts using case-based reasoning is proposed. It is composed of four major modules: retriever, modifier, simulator and repairer. This paper focuses on the representation, indexing and retrieving of process planning cases. A feature-based representation for parts and processes is presented. An effective similarity metric is developed to measure the similarity between two parts so that the most similar case can be retrieved rapidly and correctly. It is based on the hierarchical representation of part features. In addition, indexing and retrieving mechanisms are discussed in this paper. A prototype based on the proposed representation, indexing and similarity metric was developed on a Sun workstation using ACIS and 3D-Toolkit from Spatial Technology Inc.

Priority analysis is one of the most important issues in the trade-off analysis of imprecise conflicting requirements whose elasticity is captured using fuzzy logic. Requirement analysts need to know not only the relative ordering of requirements based on their importance but also how much one requirement is more important than another requirement in order to achieve an effective trade-off. This paper presents a formal approach for reasoning about relative priority by analysing a customer's trade-off preference among imprecise conflicting requirements. A possibilistic reasoning framework for inferring relative priority from case analysis under uncertainty is also developed.