A warm welcome to the 2024 IEEE World Congress on Services (SERVICES). With Professor Zhi Jin and Professor Michael Sheng serving as the Congress General Chairs, I trust everyone will have a rewarding experience participating in the IEEE Computer Society's flagship annual event in services computing, whether attending on-site or remotely.

A warm welcome to the 2024 IEEE World Congress on Services (SERVICES). With Professor Zhi Jin and Professor Michael Sheng serving as the Congress General Chairs, I trust everyone will have a rewarding experience participating in the IEEE Computer Society's flagship annual event in services computing, whether attending on-site or remotely.

The Internet of Things (IoT) has been extensively applied to human-centric smart environments. Services provisioned within these IoT-enabled smart settings can substantially enhance the quality of life, mitigate potential hazards, and thereby offer personalized services for their users. However, there is a notable deficiency in the consideration of human factors necessary for realizing more refined and personalized situation-aware services. Moreover, as learning-based approaches are widely used in providing situation analysis in the current era, the challenge of training a robust learning model is aggravated by the scarcity of locally collected user data. Federated Learning (FL) was proposed to address the issue in a centralized, cloud-edge-based setting. Nonetheless, it falls short of facilitating personalized learning, which is crucial for the provisioning of local situation-aware services. In this paper, we propose a decen-tralized, clustering-based mutual learning approach that enables each edge server to learn a personalized model by iteratively sharing knowledge within clusters formed based on situational similarities. We used connected smart homes as an example to demonstrate the learning approach, and show the effectiveness of achieving personalized situation analysis, which ultimately leads to robust and accurate service in smart environments.

With the advent of the services computing era, challenges in educating capable future software services engineers and researchers have become more pressing than ever. Software services engineers are professionals whose training cuts across computer science, software engineering, services computing, as well as relevant educational elements in management science and engineering, social science, serviceology, and service science and engineering [1]. We foresee an urgent need in this fast-emerging multidisciplinary field "Software Services Engineering" (SSE) [2] [2.1] for a comprehensive collection of education and training artifacts including well-defined body of knowledge (BOK), model curricula, open-source platforms, certification requirements, accreditation criteria, articulation directives, exemplary professional course modules, among other related components.

As information technology has been enabling services, the modern service industry, a cornerstone of the global economy, has driven development of the era of the service economy. According to the World Bank, since the 21stcentury, the service sector's share of global GDP has consistently been between 61% and 65%. “Everything as a service” has become a phenomenon in human society. Education on service science and engineering has been developed to cultivate more young people for the emerging service industry and service society.

In an enterprise-grade hybrid-multicloud computing environment, capability-providing as-a-service endpoints (or aaS-endpoints) can be deployed across diverse computing platforms, e.g., public clouds and on-prem enterprise private clouds. To ensure a seamless, unified, and enterprise-compliant acquisition of the capabilities by client applications, the presence of a cross-cloud API mediation service is crucial. However, as the number and heterogeneity of aaS-endpoints increase, delivering the API mediation service at scale becomes increasingly costly. This paper presents a robust approach to API service mediation in enterprise-grade hybrid-multicloud computing environments. It tackles the challenges, offering a distributed architecture comprising dynamically composed managed microservices, microservice zones, intelligent endpoint selection, and adaptive statistical learning (aiming to exploit localities in performance history of aaS-endpoint invocations and to facilitate adding or removing active aaS-endpoints). The successful reference implementation and$24\mathrm{x}7\mathrm{x}365$delivery in real-world settings of the approach validate its efficacy as a practical solution for API service mediation.

The emergence of blockchain technologies and the rapid growth of the Internet of Things (IoT) have brought blockchain-premised IoT (BIoT) systems into the focus of recent studies. The decentralized nature of blockchain enables data traceability, transparency, and immutability as complementary security features to the existing IoT systems. It has been applied to prevent malicious control or data leakages in traditional cloud-based, vendor-specific IoT use case scenarios. Nevertheless, as we gradually step towards the situation-aware IoT era, the lack of means to incorporate situation awareness with BIoT systems has limited the full potential of such integration. In this work, we propose a framework, Situ-Oracle, as an attempt to provide situation analysis as a service to BIoT systems. The framework utilizes a Recurrent Neural Network (RNN) based learning model to perform sensory-based situation analysis. We used smart home as an example to demonstrate the feasibility of the integration in bringing situation awareness to smart-contract-enabled IoT systems. Following that, system-wide performance evaluations were conducted over a physically constructed BIoT system, the results show that the proposed system achieves better situation analysis accuracy and network performance compared to a baseline system. Overall, the paper presents a promising approach for improving situation analysis in BIoT systems, with potential applications in various domains such as smart homes, healthcare, and industrial automation.

Mobile crowdsensing technologies augment the collective effort on exploiting data from a large crowd of mobile users in ubiquitous environments. When mobile users partake in executing crowdsensing tasks, they can receive rewards and be incentified to stay in virtual teamwork. This paper proposes a game-based incentive mechanism, named Incentive-G, aiming at recruiting mobile users effectively and improving the reliability and quality of sensing data against untrusty or malicious users. The Incentive-G mechanism consists of several design phases, including analyzing sensing data, determining reputations of mobile users, and ensuring data quality and reliability by voting in a task group. This mechanism adopts a two-stage Stackelberg game for analyzing reciprocal relationship between service providers and mobile users, and then optimizes incentive benefits using backward induction. Our analysis shows that the existence and uniqueness of the Stackelberg equilibrium can be validated by identifying the best data-provision strategies for mobile users. In addition, the maximum revenue strategy for a service provider can be found by gathering a sufficient amount of high-quality data from mobile users. Performance results manifest that the Incentive-G mechanism is able to significantly encourage mobile users to contribute their efforts and maximize the revenue for game-based crowdsensing services.

Industry observers with keen eyes have found that services computing and software engineering are becoming more and more intertwined. That is, services computing professionals and software engineers have been finding ways to intersect theories and practices that used to be more of a concern for the other camp. This emerging interdisciplinary field of study is hereby termed Software Services Engineering (SSE) [1].

Relying upon a long-standing prediction derived from one of Software Engineering’s most durable and respected software development models, we show that the primary methodological design decision shaping Scrum can be understood as a sampling-based solution to the 90% Syndrome -- a solution that exhibits exactly the characteristics predicted for such a solution. Aside from contributing credence to Scrum’s claim of improved predictability, this direct connection to the "normal science" software engineering of the 80’s and 90’s refutes the widely held assumption that Scrum (along with other Agile methods) represents a major paradigm shift or a "Kuhnian revolution" and thus requires some new "theory of agility". Instead, we argue, that Scrum should be viewed as solving the 90% Syndrome in a manner that affirms normal science, and that its sampling strategy constitutes the central methodological design choice, independently providing coherent justification for nearly every other Scrum-specific methodological design choice. We close with observations about the implications for future research and about the limitations of this work.

As we have entered the Internet-of-Things (IoT) era, further blessed with rapid advances in several key technological areas including DevOps, AI/ML, 5G/6G/, neurocomputing, to name a few, it is imperative we think big and aim high. This new venture will require professionals in both software engineering and services computing to collaborate with an unprecedented intensity, and jointly develop the new interdisciplinary field hereby named Software Services Engineering (SSE). In SSE, the ever-deepening system dynamics emerging from both environments and humans in varying contexts are imposing steep challenges to both researchers and practitioners. Humans, both developers and the vast number of end users, are embedded ever closer to IoT environments, and are being afforded ample opportunities to continuously inject inputs during system development and after deployment. In fact, humans are increasingly playing the roles of both sensor and actuator. Traditional requirements engineering researchers are being lured more than ever into exploiting the IoT environments where human users are deeply embedded, to gather contextual information that inevitably introduces lots of ambiguity and uncertainty. Provisioning of highly adaptable and scalable microservices would be key to timely meeting ever-changing human desires and ever-evolving system requirements in the nimblest manner. As such, an ultra-agile and field-programmable development methodology and environment will be imperative to achieving such ultrafine grained microservices provisioning. Such ultra-agility and ultrafine granularity requirements imposed to the services industry obligate company executives to expect extreme manageability assurance to become the centroid of system operations and administration. The ultimate goal in pursuit of such a noble dream will be to provide genuinely individualized and trustworthy service, possibly enabled by AI, but it should be both explainable and ethical. Facing such grand challenges, this declaration samples a subset of burning issues in SSE through observations in seven themes, only meant to be starting points for the SSE community to further investigate. Through our declarations we also call for heightened attention to an assorted array of existing, barely emerging or non-existent services computing and software engineering methods for a concerted effort to research and explore.

Software comprehension is an important part of software maintenance. To understand a piece of large and complex software, the first problem to be solved is where to start the understanding process. Choosing to start the comprehension process from the important software elements has proven to be a practical way. Research on complex networks opens new opportunities for identifying important elements, and many approaches have been proposed. However, the software networks that existing approaches use neglect the multilayer nature of software systems. That is, nodes in the network can have different types of relationships at the same time, and each type of relationship forms a specific layer. Worse still, they mainly focus on identifying important classes, and little work has been done on quantifying package importance. In this paper, we propose an ElementRank approach to provide a ranked list of classes (or packages) for maintainers to start the comprehension process. The top-ranked classes (or packages) can be seen as the starting points for the software comprehension process at the class (or package) level. First, we introduce two kinds of multilayer software networks to describe the topological structure of software at the class level and package level, respectively. Second, we propose a weighted PageRank algorithm to calculate the weighted PageRank value of classes (or packages) in each layer of the corresponding multilayer software network. Then, we use AHP (Analytic Hierarchy Process) to weigh each layer in the corresponding multilayer software network, and further aggregate the weighted PageRank value to obtain the global weighted PageRank value for each class (or package). Finally, all the classes (or packages) are ranked according to their global weighted PageRank values in a descending order, and the top-ranked classes (or packages) can serve as the starting points for the software comprehension process at the class (or package) level. ElementRank is validated theoretically using the widely accepted Weyuker’s criteria. Theoretical results show that the global weighted PageRank value for classes (or packages) satisfies most of Weyuker’s properties. Furthermore, ElementRank is evaluated empirically using a set of twelve open source software systems. Through a set of experiments, we show the rank correlation between the results of ElementRank and that of the approaches in the related work, and the benefits of ElementRank are also illustrated in comparison with other approaches in the related work. Empirical results also show that ElementRank can be applied to large software systems.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Early-stage dementia impacts aging adults' cognitive functioning including the initiative and performance of instrumental activities of daily living (IADLs) that require a series of actions to be completed (e.g., meal preparation, medication management). Thus, cognitive impairment predisposes adults to temporal organization of action sequence error in IADLs that may constitute life-threatening injuries or accidents [11]-[13]. To mitigate this inherent risk in the performance of IADLs, a linear temporal logic (LTL)-based safety model is proposed for detection of risky behaviors inherent IADLs in smart home environments.

Cloud based vehicular edge computing is a promising technology to deliver quality of services for accessing public cloud from vehicles. As in-vehicle computers (e.g. onboard Android devices) are typically energy and resource constrained, they need to operate energy-efficiently. Due to the high cost in building sustainable infrastructure to support vehicular edge computing systems, edge services must also operate with high energy-efficiency for better acceptability and applicability in the market place. In this paper, we present a novel situation-enabled framework to enhance energy-efficiency of both in-vehicle computer applications and Mobile Edge Computing (MEC) services. The framework consists of three components. First, we collect a user's in-vehicle driving situation data and use the data to train Long Short Term Memory (LSTM) deep learning model. The model is used to predict the user's future situation, determine whether the requested application is allowable, and manage the allowable application pertaining to the predicted situation. Second, from vehicles to MEC servers, we optimize the energy consumption in request routing. Third, a Breadth First Search (BFS) based offloading algorithm to coordinate the placement of servers in the MEC servers to save the energy consumption in the server pool. Implementation of LSTM on top of a situation model and simulation of the vehicular edge offloading have been conducted to validate the energy efficiency of the proposed framework. The results show that the performance of our proposed offloading algorithm can be at par with the most aggressive energy saving variant.

Abstract Older adults with early‐stage dementia (ED) can experience confusion or lack clarity when performing routine activities of daily living (ADLs). These circumstances predispose the older adult to safety‐critical and often risky situations. A safety‐critical risky situation is one that constitutes a hazard. To support independent living, a sensor‐laden smart environment can be employed to mitigate such hazards. In this paper, we propose a situation‐centered goal reinforcement framework that supports older adults with ED in their decision making, and guides them through their ADL in order to fulfill their goal or intention and avoid hazards. First, we employ an LSTM (Long Short‐Term Memory) model to infer the current goal of the resident, using their previously observed normal ADL patterns. Secondly, we identify potentially risky situations in their currently observed goal path. We then incorporate a situ‐learning agent (SLA) that helps an inhabitant to make the right decision, thus preventing adverse events while guiding her through the task sequence that leads to her goal state. In addition, we use a naïve agent to simulate episodes of confusion similar to those that might be experienced by older adults with ED. We validated our method against an open‐source dementia dataset (Quesada et al., 2015) by considering four types of ADLs as case studies. We achieved an accuracy of 90.1% for our goal inference model, higher than the accuracies reported by related studies. We also reported other metrics including precision, recall and f1‐score for goal inference model. Finally, SLA's action recommendations relevance was evaluated accordingly.

The following topics are dealt with: learning (artificial intelligence); Internet; mobile computing; security of data; pattern classification; program testing; Internet of Things; smart phones; neural nets; data mining.

Over the last twenty years, authors of systematic literature reviews have repeatedly criticized the state of agile research, describing it as nascent, random, and lacking appropriate recruitment strategies. This paper highlights some historical misperceptions and assumptions that tend to misdirect researchers, especially those new to the field. It also calls attention to some challenges intrinsic to agile research and comments on how those challenges impact research design. The intent is to help new researchers more easily make sense of the existing literature, so that they can focus their energy and resources on meaningful contributions to the field.

In this paper, we propose a new recommendation system that considers the changing human mental state, behavior and environmental contexts to provide guidance to persons diagnosed of mild cognitive impairment (MCI) or Alzheimer's in performing their activities of daily living (ADLs). The recommendation system is based on the Situ-framework that represents an activity as a sequence of situations, and a situation is a 3-tuplewhere M denotes human mental state, B is the behavior context and E is the environmental context. More so, it uses an automaton to model an activity thereby generating corresponding task sequences. Interactions between the inhabitants and the sensor networks and the activity being performed are represented as an information space. It then uses a modified model learning algorithm as its decision making tool to recommend appropriate actions or tasks sequence to the inhabitant in a situation of uncertainty caused by episode of confusion or memory loss to ensure he reaches his goal. Consequently, this helps ensure that safety of the person is not compromised when he makes a poor decision regarding the sequence in which ADL tasks are to be performed. We use a single activity class dataset generated from a smart home for 50 independent learning experiments for three (3) ADLs case studies. The accuracy of the sequential-tasks recommendations was found to be high. We further evaluated the risk sensitivity of the recommendation system using the action selection probabilities and corresponding reward points associated with performing a task. The results show that the proposed recommendation system is risk averse.

Non-functional requirements (NFRs) play a crucial role in all the downstream activities of a software life-cycle process. Capturing newly emerged NFRs is key to software evolution. Recent research shows functional requirements in the form of task-level alternative features can be elicited from user behavioral and system contextual data through user goal inference. Considering the close connection between the concept of goal and desire, we posit that there is an opportunity to extract new NFRs based on users' mental states, particularly their desires. We propose to use a statistical model to infer desires with multiple-levels of abstraction based on contextual data under Situ framework. Our multi-layered desire inference method takes inference confidence into consideration, and tries to make sense of inference results with both high-and low-inference confidence. By utilizing the different abstraction levels of desires, we provide an illustrative example with three cases to elicit users' new NFRs including new high-level and low-level desires and new contributing relationships between them. Several implications of this work are also discussed. We plan to conduct experiments on human subjects to validate the proposed method as IRB has just approved our proposal.

With the prevalent usage of mobile phones, energy saving has become a critical issue for manufacturers. Extended battery life with increasing battery capacity is a common solution for hardware-based improvements. However, energy efficient management mechanism has attracted matching attention. In this paper, we present a situation based energy management framework. First, we collect data of activities of daily living (ADL) and apply deep learning for training. Next, Long Short Term Memory (LSTM) of Recurrent Neural Network (RNN) is used as our forecasting model to predict what a user will do at a certain hour in a certain day. Such prediction model relies on sensor fusion in gesture detection, camera-based facial detection and touch events on LCD screen in dynamic contextual environment. Finally, an inference engine is used to recommend actions to be takes for energy saving by digesting the information gathered on previous two stages, static information collecting and dynamic detection. In order to show the performance of our framework, we make use of external hardware as assistance tool which is suggested by Google developer's website.

The three papers in this special section were presented at the 14th International Conference on Smart Homes and Health Telematics (ICOST 2016) that was hosted by Huazhong University of Science and Technology in China. Addresses the emergence of Digital Healthcare where there has been a sharp increase of activities in research, design, development, real-world deployment and evaluation of smart environments, assistive technologies, medical robotics and health telematics systems by employing computing and communication technologies in the health domain. Digital Health can be described as convergence of digital and genomic technologies with health, healthcare, living, and society to enhance the efficiency of healthcare delivery and make medicine prescription and medical treatments more personalized and precise.

With the advent of technologies such as Internet of Things (IoT) and Cloud Computing we have now entered the Big Data era. In particular, in a modern sensor-laden environment, often called smart environment, we expect that a huge amount of data can be generated from many sources including hardware and software sensors, and oftentimes humans as sensors too. The significant amount of data thus generated can potentially help smart environments make situation-aware data-driven decisions based on the predicative capability of future situations in a timely manner. However, massive data requires efficiency in data processing. Therefore, scalable solutions will be required to allow smart environments to provide effective and efficient decisions. This paper presents a framework for situation-aware data-driven decision making in smart environments using the MapReduce.

Human factors have been increasingly recognized as one of the major driving forces of requirement changes. We believe that the requirements elicitation (RE) process should largely embrace human-centered perspectives, and this paper focuses on changing human intentions and desires over time. To support software evolution due to requirement changes, Situ framework has been proposed to model and detect human intentions by inferring their desires through monitoring environmental and human behavioral contexts prior to or after system deployment. Researchers have reported that Situ is able to infer users' desires with high accuracy using the Conditional Random Fields method. However, manual analysis is still needed for new intention identification and new requirements elicitation. This work attempts to find a computable way to identify users' new intentions with minimal help from human oracle. We discuss the feasibility of implementing the concept of DIKW (Data, Information, Knowledge, Wisdom) to bridge the gap between user behavioral & contextual data and requirements, and propose a situation-centric approach using the Multi-strategy, Task-adaptive Learning (MTL) method. A case study shows that the proposed approach is able to identify users' new intentions, and is especially effective to capture alternatives of low-level task.

In this paper we present a new human-centered requirements elicitation methodology that effectively considers end-user's desire, behavioral and environmental contexts. We follow a methodology that uses a computationally rich definition of situation as a 3-tuple where d denotes human desire, A denotes the action vector, and E denotes the environment context vector. The proposed method of human-centered requirements elicitation is based on the situation -- transition structure which is a directed weighted graph that represents transition from one situation to another. We illustrate the proposed methodology through some case studies with open access data sets. Requirements thus elicited appear to be valid after manual inspection. Future directions along this line of research are then asserted.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Presents an editorial which explores the market fr cloud-based mobile computing systems. The concept of cloud-based mobile applications has emerged recently to address the limitations of pure mobile applications by leveraging the vast resources available at computing clouds. Traditionally, mobile applications benefit by outsourcing a part of the program execution to a cloud-based system to compute the (intermediate) results and send back these results for integration by the relevant mobile application. By doing so, the program code, data as well as device-specific information, such as user locations, can be made available to a cloud. Infrastructures to support computation outsourcing of the above type and its variants have been widely studied; and the associated security and privacy issues are hot topics in research.

Presents a listing of the Symposium organizing committee.

User's new requirements on a system are critical factors for driving software service evolution. New requirements usually arise when users are not satisfied with the existing system as often reflected in users' divergent behaviors that can be detected through comparison with the known behavior patterns. In this paper, we propose a methodology that applies Conditional Random Fields (CRF) as the mathematical foundation for inferring users' desires based on a peculiar form of observations and further exploring their new intentions that often imply new requirements. The potential new intentions detected by the CRF model will be verified and analyzed to elicit users' new requirements for the system. As a result, the system should evolve through modifications or acquiring new functionalities to satisfy the new requirements. An experiment on a research library system is conducted to demonstrate how the new intentions are detected using the CRF method and used to drive system evolution.

Applications with a Graphical User Interface (GUI) front-end are ubiquitous, thus it is of great importance to assure the quality of such systems. Model-based GUI testing provides an effective mechanism for automated testing of applications with a GUI. However, most existing model-based GUI test generation techniques produce many infeasible (i.e., Broken) event sequences which are used as test cases. This happens primarily because they ignore behavioral specifications of the GUI under test. In this paper, we present an automated black box framework that reveals an important set of GUI invariants as state-based event constraints. GUIDiVa, an iterative algorithm at the core of our framework, enumerates all possible constraint violations as potential reasons of a test case failure, on the failed event of the infeasible test case. It further selects and adds the most promising constraints of each iteration to a final set based on our notion of constraint Validity Weight. The results of empirical studies on both seeded and nine non-trivial study subjects show that our framework is capable of capturing important aspects of GUI behavior, while considerably reducing the number of insfeasible test cases. Additionally, a human oracle is used to further validate the accuracy of the discovered constraints.

Evolution of software components may lead to compatibility problems. State-of-the-art fully-automatic compatibility solutions either require source code, or they fail to support refactorings that may lead to conflict method signatures, such as changing the order of same-typed parameters or changing return types. Moreover, all of them will statically modify source or binary files, which may violate the license agreements of components. In this paper, we propose ALTA, a fully-automatic adaptation technique for refactoring-based evolution of software component. ALTA supports refactorings that may lead to conflict method signatures. In addition, ALTA can perform load-time adaptation, and therefore it can work under all kinds of license agreements. We also present ALTA*, an implementation of ALTA by Java and AspectJ. The evaluation results show that ALTA* effectively solves compatibility problems in real-world components with a performance overhead of load-time adaptation feature (if enabled) at around 11%.

With the advancement of miniature sensors, wireless networking and context awareness, the importance of data-intensive computing is on the rise, with practical applications such as web categorization and data mining. One of the critical challenges in data-intensive computing is data clustering, as effective clustering algorithm will enable researchers and automated systems to analyze and organize massive amount of data much more efficiently. Many data clustering algorithms already exist, but most require a priori knowledge on the number of classes to guide the clustering process. We propose Auto_Ant_TMs_Shape, a two-phase algorithm, for automatically forming optimal number of clusters with arbitrary shapes. The first phase uses the hybrid approach of K-means and enhanced Ant-based template mechanism to generate small seed clusters with high purity in each cluster. In the second phase, small clusters are iteratively merged to obtain the final clusters using a merging algorithm. We apply Auto_Ant_TMs_Shape to 8 widely-used datasets, and compare the clustering results with two approaches based on density-based algorithm (DBSCAN) and Particle Swarm Optimization (PSO). The results show that Auto_Ant_TMs_Shape is very effective and thus achieve good clustering results in near optimal number of clusters without knowing the number of classes in advance.

Positioned as the backbone of service asset management console, a service registry has to enable real-time and offline service selection in an effective manner. This paper presents an analytic algorithm that is used to guide the architectural design of service exploration in a service registry. Service assets are proposed to be framed into a well-established categorical structure based on pattern recognition algorithm. This design aims to provide systematic methodology and enablement architecture for analyzing, clustering, and adapting heterogeneous services for dynamic application integration. The exploitation of pattern recognition algorithm maps a large amount of services into a manageable feature space, which consists of attributes that are related to static description and dynamic features, such as historical QoS and service-level agreement. The proposed architecture and associated service exploration methodology have been integrated into an industry strength service-oriented architecture solution design platform. We also present a case study using the developed platform to illustrate the proposed algorithm for business service clustering and selection.

User problems encountered during the use of a software product are often hard to identify even after the software product is thoroughly-tested and then released. There are inevitably unexpected situations introduced or triggered by transient use patterns, however, these unexpected situations can be hardly eliminated due to various human factors on user interaction. This study of situation-oriented real user monitoring develops an application of concept lattice to represent use patterns from user interaction and perceive potential user problems. Data structure of user problem is modeled based on user problem ontology. Once a user problem is recognized, observation data of the use patterns are the subject for further analysis. In this paper, use patterns in a file upload service are used to demonstrate how the concept lattice is used and discuss about validity of the causal factors found in the concept lattice.

State diagrams commonly used for describing dynamic behavior of systems or objects can have various forms that specify the same behavior. Highly understandable state diagrams are important for efficient and clear communication between developers. Measurement of understandability of state diagrams is the first step to obtain highly understandable ones. Unfortunately, there is little research on metrics for evaluating state diagram's understandability. In this paper, we propose cohesion and coupling metrics, named Average Cohesiveness Of States (ACOS) and Average number of Similar States Of States (ASSOS), to evaluate understandability of state diagrams. To validate effectiveness of the metrics, we compared understandability of three state diagrams of different representations but the same behavior for two systems. The experiments showed that ACOS and ASSOS can capture understandability more correctly than existing structure-based metrics.

While specialized knowledge and skills are the hallmark of modern society, the size and complexity of contemporary problems often require cooperative effort to analyze and solve. Therefore, experiences with skills, methodologies, and tools for effective interdisciplinary collaboration and structured problem solving are vital for preparing students for future academic and professional success. Meanwhile, computational systems have permeated much of modern professional and personal life, making computational thinking an essential skill for members of modern society. However, formal training in these techniques is primarily limited to students within computer science, mathematics, management of information systems, and engineering. At Iowa State University, we have designed and offered an experimental course to develop undergraduate students' abilities for interdisciplinary teamwork and to disseminate computational thinking skills to a broader range of students. This novel course was jointly designed and instructed by faculty from the Computer Science Department, Gerontology Program, and Graphic Design Program to incorporate diverse faculty expertise and pedagogical approaches. Students were required to interview real users to identify real-life problems, gather requirements, and assess candidate solutions, which necessitated communication both within the group and with technologically-disinclined users. In-class presentations and wiki-based project websites provided regular practice at disseminating domain expertise to larger interdisciplinary audiences. Workshops, group-based mentoring, peer learning, and guided discovery allowed non-CS majors to learn much more about computer programs and tools, and grading criteria held students individually accountable within their disciplines but also emphasized group collaboration

In order for the intelligent assistant systems to provide users with timely and appropriate assistances, most of them focus on what is considered to be the "rational" aspect of the user behaviors. However, since human desire is the fundamental driving force of human behaviors, without including users' desires, the system cannot provide most appropriate responses. We propose a hierarchical desire inference process based on the Bayesian Belief Networks (BBNs), that considers the affective states, behavior contexts and environmental contexts of a user at given points in time to infer the user's desire. The inferred desire of the highest probability from the BBNs is then used in the follow-up decision making.

An important goal of automatic testing techniques, including random testing is to achieve high code coverage with a minimum set of test cases. To meet this goal, random testing researchers have proposed many techniques to generate test inputs and method call sequences that yield higher code coverage. However, most proposed random testing techniques are only suitable for toy systems, and they achieve low code coverage rates while generating too many unnecessary test cases on large-scale software systems. We propose GENRED, a tool that utilizes three approaches: input on demand creation and coverage-based method selection techniques that enhance Randoop, a state-of-the-art feedback-directed random testing technique, and finally, a sequence-based reduction technique that removes redundant test cases without executing them. We evaluate GENRED as a tool to test four open-source systems. The results show that these techniques achieve branch coverage improvement by 13.7% and prune 51.8% of the test cases without sacrificing code coverage.

While software evolution has been studied extensively in software engineering, few of these efforts have involved a systematic exploration of human epistemological attitudes, such as human desire and intention, as the driving force of software service evolution. Our work proposes a theoretical framework to monitor and reason about human intention and its changes, which in turn can be used to determine how software and services should evolve to be individualized and better serve each user. Extending the Situ framework, we explore the service satisfiability problem through sub-world coverage following Kripke semantics, which enjoys wide application in AI and other fields related to human epistemic reasoning.

Service-oriented architecture (SOA) provides a scalable and flexible framework for service composition. Service composition algorithms play an important role in selecting services from different providers to reach desirable QoS levels according to the performance requirements of composite services, and improve customer satisfaction. This paper proposes a novel QoS-based dynamic service composition technique for web services with Ant Colony Optimization (ACO) in an optimization approach. The novelty of this work lies with our multi-objective optimal-path selection modeling for QoS-based dynamic web service composition and a new version of ACO algorithm that is proposed to solve this multi-objective optimization problem. The experiments show that the new version of ACO algorithm is very efficient in solving such a problem.

Testing object-oriented (OO) software is critical because OO languages are commonly used in developing modern software systems. In testing OO software, one important and yet challenging problem is to generate desirable object instances for receivers and arguments to achieve high code coverage, such as branch coverage, or find bugs. Our initial empirical findings show that coverage of nearly half of the difficult-to-cover branches that a state-of-the-art test-generation tool cannot cover requires desirable object instances that the tool fails to generate. Generating desirable object instances has been a significant challenge for automated test-generation tools, partly because the search space for such desirable object instances is huge, no matter whether these tools compose method sequences to produce object instances or directly construct object instances. To address this significant challenge, we propose a novel approach called Object Capture based Automated Testing (OCAT). OCAT captures object instances dynamically from program executions (e.g., ones from system testing or real use). These captured objects assist an existing automated test-generation tool, such as a random testing tool, to achieve higher code coverage. Afterwards, OCAT mutates collected instances, based on observed not-covered branches. We evaluated OCAT on three open source projects, and our empirical results show that OCAT helps a state-of-the-art random testing tool, Randoop, to achieve high branch coverage: on average 68.5%, with 25.5% improved from only 43.0% achieved by Randoop alone.

Data clustering algorithms play an important role in effective analysis and organization of massive amounts of information. The K-means algorithm is the most commonly used partitional data clustering algorithm because of its simplicity in implementation and its high convergence rate. However, it suffers from the inability to always converge to the global optima, depending on how the data items are distributed initially. Ant-based Template Mechanism (Ant_TM) is another frequently used clustering algorithm, but it exhibits two major weaknesses in convergence rate and data purity of clustering results. In this paper, we first present a modification to the original Ant_TM to encourage formation of new cluster regions that enables the clustering result to move away from local optima. Second, we present two hybrid clustering algorithms based on the enhanced Ant-based Template Mechanism (Ant_TM) and the K-means algorithms. The rationale is that the integration of the K-means algorithm can speed up the convergence process and provide a perturbance to break free from local optimum clustering. We conduct experiments to compare the performance of our hybrid algorithms, against the enhanced Ant TM and the K-means algorithm, as well as the PSO+K and GA. The result shows that our algorithms outperform the original Ant_TM, K-means, and PSO+K, and is competitive against the GA in terms of the more compact and better separated clusters.

We propose a design methodology, Service-Oriented Design with Aspects (SODA), for service-oriented systems to address the need to continually upgrade and evolve services while maintaining various versions. Our approach treats aspects as first-class design elements and consistently applies the concept of aspect to all phases of design and evaluation. At the early design stages, crosscutting concerns are first separated out as aspects, and then, services are composed by weaving the different design elements together. The behavior of aspects and services is represented as basic Petri Nets and we present rules for weaving together Petri Nets so as to obtain behavior of the integrated system (with aspects crosscutting services). Even at the evaluation stages, performance and resource data are separated out as aspects to be woven in to the design so as to enable advanced analysis using Petri Net tools. A small order service example is used to illustrate our approach.

The Service registry, the yellow pages of Service-Oriented Architecture (SOA), plays a central role in SOA-based service systems. The service registry has to be scalable to manage large number of services along with their requirements on storage and discovery. Based on our previous work on feature-based services quantification, we characterize services according to their diverse functional and non-functional requirements, and represent them as string formats which can be stored, probed, and indexed by efficient data structures, such as hash table and Bloom filter. Then, we propose a comprehensive service-storage solution using the counting Bloom filter (CBF). The application of CBF enables us to structure candidate services into separate groups, resulting in an accelerated services discovery process. The contributions of this research work include a new approach to manage large number of services based on quantified service features, and a storage architecture design to support service discovery. Experimental results strongly support these claims.

Evolvability is essential for computer systems to adapt to the dynamic and changing requirements in response to instant or delayed feedback from a service environment that nowadays is becoming more and more context aware; however, current context-aware service-centric models largely lack the capability to continuously explore human intentions that often drive system evolution. To support service requirements analysis of real-world applications for services computing, this paper presents a situation-theoretic approach to human-intention-driven service evolution in context-aware service environments. In this study, we give situation a definition that is rich in semantics and useful for modeling and reasoning human intentions, whereas the definition of intention is based on the observations of situations. A novel computational framework is described that allows us to model and infer human intentions by detecting the desires of an individual as well as capturing the corresponding context values through observations. An inference process based on Hidden Markov Model makes instant definition of individualized services at runtime possible, and significantly, shortens service evolution cycle. We illustrate the possible applications of this framework through a smart home example aimed at supporting independent living of elderly people.

This paper proposes an Input Adjustable Tree Algorithm for the flag problems of evolutionary testing (ET). With the algorithm, the dependencies of input and internal/flag variables can be determined. Based on that, ET guides the search efficiently with the presence of flag variables in the source code.

Changing system requirements, especially for context awareness (CA) systems, often cause modifications in the software systems in order to adapt to dynamic environments. Since the requirements may become temporarily obsolete or contrary to human intentions, the CA systems need to be tuned to resolve the conflict. On the other hand, most CA design methods rely on pre-defined requirements and reasoning engine, thus, fail to address all the possible situations. Consequently, services provided by such a CA system are limited to accommodate some situations and unable to react as expected. Therefore, it is critical for CA systems to capture exceptions at runtime, infer changed human intentions, and adapt to these changes. This study focuses on inference of ever-changing human intentions and monitoring human intentions to handle system evolution. In this paper, we present an inference mechanism of human intentions via the human-machine dimensional inference ontology (HDIO). This ontology gives inference rules based on the BDI logic to deduce human intentions from contexts. Furthermore, the inference exercises of a healthcare system example shows how user intentions relate to system requirements and how they help improve self-adaptability of CA systems.

User intentions to obtain services evolve over time. Since the changes of intention may occur at any time, some system requirements may become temporarily obsolete or contrary to a user intention. However, user intentions often indicate potential, valuable goals for upgrading the system to a new version or engaging new development. Our research tackles requirements analysis issues via feedback from context awareness systems to identify user intentions and capture instant definitions of goal in a robust manner. This paper presents a context-aware goal elicitation process by exploring the aspects of data, information, knowledge and wisdom. Furthermore, captured user intentions and goals are shown in a case study of healthcare system, and issues for the goal elicitation are explored.

This paper aims to identify the missing links from theory of Genetic Algorithms (GAs) to application of GAs.

Maintaining traceability links among software artifacts is particularly important for many software engineering tasks. Even though automatic traceability link recovery tools are successful in identifying the semantic connections among software artifacts produced during software development, no existing traceability link management approach can effectively and automatically deal with software evolution. We propose a technique to automatically manage traceability link evolution and update the links in evolving software. Our novel technique, called incremental latent semantic indexing (iLSI), allows for the fast and low-cost LSI computation for the update of traceability links by analyzing the changes to software artifacts and by reusing the result from the previous LSI computation before the changes. We present our iLSI technique, and describe a complete automatic traceability link evolution management tool, TLEM, that is capable of interactively and quickly updating traceability links in the presence of evolving software artifacts. We report on our empirical evaluation with various experimental studies to assess the performance and usefulness of our approach.

Many problems in software engineering (SE) can be for mulated as optimization problems. Genetic algorithm (GA) is one of the more effective tools for solving such opti mization problems and has attracted the attention of SE re searchers in recent years. However, there is a general lack of sound support theory to help SE researchers investigate the applicability of GA to certain classes of SE problems. Without such a theory, numerous attempts to conduct a wide spectrum of experiments for solution validation appear to be ad hoc and the results are often difficult to generalize. This paper reports a foundational study to develop such a support theory. Some preliminary results are also given.

Privacy is a known barrier to the acceptance of ubiquitous computing technologies because they require individuals to trade control of their personal information and personal spaces for improved quality of life and assistance with daily activities. Previous work has been done to analyze and protect privacy in ubiquitous computing environments, but such efforts do not include a formal underlying model. We seek to approach the privacy problem from a different perspective. Namely, we seek to propose, verify, and analyze a formal model of privacy for these environments. In this paper, we discuss the beginning stages of our model, namely the resources that need to be protected, the guidelines for constructing the model, and the high-level components of our personal privacy model.

Personnel risk is an issue which has not been researched well but plays an important role to determine whether a software project succeeds or fails. Most existing research workfocuses on subjective expertise while an objective view is lacking. Furthermore, to the best of our knowledge, the demand for an automatic tool to support risk management has not been answered yet. In this research, based on objective historical data, we extend our earlier model, cability- based scheduling framework, by including risk analysis. Hence we provide a novel approach to mitigate personnel risks while achieving a project schedule with minimum cost.

As dynamic as software development, software artifacts are also constantly in evolution. As a result, traceability links among them are also changing over time. Even though traceability link recovery (TLR) tools have been successful in generating traceability relations among documentation and source code, they work on a snapshot of the artifacts at a particular time. Traceability link evolution has not been well-addressed. This hinders developers from having good understanding of the evolution of software. In this paper, we describe our incremental approach to traceability link recovery and management with the latent semantic indexing method. To complement with that approach, we suggest the use of the versioned hypermedia technology (VH). Traceability links can be consistently stored, versioned, and managed across different types of software artifacts.

In our earlier work, competitive Web services market was proposed to address one of the major business concerns, namely "competitiveness", of the current Web services research. This paper aims to attack the issues of how to model and analyze competitive Web services market, which ranges from the service node at the micro level to the competitive composite service providers at the macro level. Moreover, in order to tackle and contain the randomness embedded in a Web services system, we apply the Markov chain theory to analyze the availability of Web services market. Moreover, we analyze the stochastic performance of the Web services market using queuing theory and propose the adaptive control methodology to improve the performance.

Today, more and more software are augmented with service-oriented packaging. At the same time, more and more business and government services are provided and offered in the form of software. However, there are debates on whether and how much service engineering has in common with software engineering.

The rapidly emerging technology of Web services paves a new cost-effective way of engineering software to quickly develop and deploy Web applications by dynamically integrating other independently developed Web-service components to conduct new business transactions. This paper reports our efforts on designing and developing a Web service of pass-through authentication (PTA) for 12 online electronic-payment Web applications. In accordance with how a PTA service is developed and integrated with a corresponding back-end e-payment system, our strategies can be categorized in three stages: end-to-end integration stage, Web-services-enabled stage, and Web-services-oriented stage. Derived from real-world industrial experience, this three-stage pathway can be applied to a broad range of Web-application development projects to guide smooth transformation from a specific application-oriented design and development model toward a reusable Web-services-oriented model. Furthermore, this paper contributes to an engineering process that leads to practical Web-services-oriented software development. New research issues revealed by this project are also reported.

Improving software engineering education in Asia is more urgent than ever. The sheer size of Indian and Chinese educational systems has been producing a very large number of computer professionals from these two most populous countries in the world. Some of the graduates are sufficiently trained to engage the "IT" career at a rather low level of the IT eco-system, namely programming, routine maintenance, system operation, and lowlevel software testing tasks. There are a sufficiently large number of software architects in India, but not in China. However, based on my observation, neither country has actually developed adequate and robust software engineering education and training programs that may have hindered the further progress of their software industries. In recent years some name brand companies, namely IBM, Microsoft, and the like, began training programs in India and China to train so-called software engineers. Nevertheless, such programs are vendor-based and product-specific thus would not be able to produce software engineers who can work independently from a product line offered by such vendor

Petri net has been widely used for modeling software systems due to its mathematical soundness and support of various tools. In many cases, performance-related analyses of Petri net for a software system need to consider the resource limitations caused by a specific platform. In terms of aspect-oriented approach, the interference of various resources scattered across a software system can be regarded as a specific concern that is only necessary during its performance-related analysis process. To capture the interactions of resources within the Petri net-based software architectural model, we propose an aspect-oriented resource composition model and the XML-based representation language called the resource extension markup language (ReML) for its description. In our approach, one or more resource composition models can be developed and described in ReML separately from the development of base software architectural models. Through the weaving process, a resource composition model can be applied to extend several Petri net-based software architectural models. The resource weaver generates an augmented Petri net used for analyzing performance characteristics of the extended software architectural model with resource interactions. Our aspect-oriented approach facilitates selecting an optimal or superior resource composition model for a software architectural model, and vice versa, which is illustrated using two exemplary server models

It is well known that errors caught in the early stages of system development life cycle greatly helped reduce the expense for later error fixing and mitigate risks and complications. Performance analysis is one of the methods to ensure satisfactory system performance that can avoid redesign and patch in the later stage of system development when performance problems emerge. Much work has been done to address such concerns collectively known as the ‘performance engineering’ problem. However, a comprehensive method is still needed to provide an end-to-end support for performance evaluation on software architecture design. Automated tools to support the method must be developed, and its validation must be carefully planned and conducted. In response to this need, we propose our approach, PSIM (performance simulation and modeling). PSIM is a performance simulation and modeling tool that integrated performance properties into software architecture specifications expressed in several major UML diagrams. PSIM models can be transformed into Colored GSPN (colored generalized stochastic Petri nets) via an automated tool. As a result, the Colored GSPN models can be simulated to perform model-based performance evaluation. In this paper PSIM is first briefly reviewed and illustrated through modeling a web-based electronic conferencing system, called M-Net, to derive performance metrics. We then conduct runtime performance testing to the implementation of M-Net and compare the simulation results against the runtime testing data. PSIM is shown to be effective in predicting system performance and in identifying system performance bottlenecks through this experimentation.

Performance analysis gains more attention in recent years by researchers who focus their study on the early software development stages to mitigate the risk of redesign as problems emerge later. Previously we proposed PSIM (a performance simulation and modeling tool) that integrated performance properties into software architecture specifications expressed in several major UML diagrams. PSIM models can be transformed into colored GSPN and the colored GSPN models can then be simulated to perform model-based performance evaluation. In this paper we briefly review the PSIM approach and apply it to model a Web-based conferencing system, called M-Net, to derive performance metrics. We then conduct runtime performance testing to the implementation of M-Net and compare the simulation data to runtime testing data. The comparison results show the effectiveness of the PSIM method in predicting system performance and identifying system performance bottlenecks.

Formal electronic conferences (FEC) refers to online meetings for a geographically distributed group of people that are regulated by a rigorous set of rules. FEC technologies enable organizations to replace face-to-face business meetings with trustworthy virtual online meetings. In This work we present a Robert's rules of order (RRO)-compatible, motion-driven discussion-thread-centered meta-model, which is capable of uniformly modeling formal electronic conference activities. A tailored computerized mechanism, the collaboration description language (CODL) and its runtime environment, is also developed to formalize the model. The CODL virtual machine adds a layer of encapsulation that decouples FEC applications from underlying platforms: therefore, the development of FEC applications will become more reliable, efficient, and secure. Our preliminary experience with this meta-model is also reported.

The emerging paradigm of Web services opens a new way of Web application design and development to quickly develop and deploy Web applications by integrating independently published Web services components to conduct new business transactions. As research aiming at facilitating Web services integration and verification, WS-Net is an executable architectural description language incorporating the semantics of colored Petri-net with the style and understandability of object-oriented concepts. WS-Net describes each Web services component in three layers: interface net declares the services that the component provides to other components; interconnection net specifies the services that the component acquires to accomplish its mission; and interoperation net describes the internal operational behaviors of the component. As an architectural model that formalizes the architectural topology and behaviors of each Web services component as well as the entire system, WS-Net facilitates the verification and monitoring of Web services integration.

Electronic meeting control has been the focus of many research efforts; however, extensively adopted standards are still unavailable. In this paper, we present a generic model for electronic meeting control. Our model is based on the formal meeting protocol commonly known as the parliamentary procedure or Robert's Rules of Order (RRO). Capturing the essential features of traditional RRO, we suggest extension mechanisms in order to exploit the capabilities of electronic media. Centered on the concept of a discussion thread, our extended RRO model can handle concurrent meeting activities, multiple floors, amendment nesting, and discussion hierarchies. M-Net, an electronic meeting system, is introduced as an example supported by our model.

Web application development differs from the development of traditional software in several significant ways; therefore requirements engineering for Web applications entails new demands accordingly. This paper proposes an extreme Web requirements engineering - mockup-driven fast-prototyping methodology to help elicit and finalize system requirements, as well as facilitate adjustment to quickly changing user requirements typical to Web applications. Supporting the inclusion of customer feedback early in the development process, this strategy minimizes the risk of wasting valuable development efforts because of ambiguous or incomplete specifications. Real-life experiences of the use of the methodology in industry are reported as examples.

Computer supported cooperative work (CSCW) research is the discipline that explores how to utilize computing and networking technologies to facilitate cooperation and collaboration among people. A variety of research has been conducted on CSCW architecture. However, how to ensure CSCW system effectiveness and efficiency on supporting collaboration remains a challenge. In this paper we present a rule mitigated framework for CSCW applications. This research contributes to the state of the art by presenting a robust and comprehensive architecture to support distributed collaboration.

Although the benefits of requirements traceability are widely recognized, the actual practice of maintaining a traceability scheme is not always entirely successful. The traceability infrastructure underlying a software system tends to erode over its lifetime, as time-pressured practitioners fail to consistently maintain links and update impacted artifacts each time a change occurs, even with the support of automated systems. This paper proposes a new method of traceability based upon event-notification and is applicable even in a heterogeneous and globally distributed development environment. Traceable artifacts are no longer tightly coupled but are linked through an event service, which creates an environment in which change is handled more efficiently, and artifacts and their related links are maintained in a restorable state. The method also supports enhanced project management for the process of updating and maintaining the system artifacts.

Although requirements traceability is crucial in both the development and maintenance of a software system, traceability links and related artifacts tend to deteriorate, as time-pressured practitioners fail to systematically update them in response to change. Event-based traceability addresses this issue by establishing links through a loosely coupled publisher/subscriber scheme. Dependent entities subscribe to requirements and receive event notifications as changes occur. This paper focuses upon the role played by the requirements specification as a publisher of events. A set of standard change events is defined and a method for monitoring a user's actions within a requirements management environment and the subsequent recognition and publication of the change events is proposed. Early results obtained from testing this approach are reported.

Posing speculative questions about a software system is an important yet often unsupported activity. Current impact analysis techniques tend to focus upon the functionality of the system, whilst the effects of change upon performance requirements are largely ignored until after implementation. This tendency can lead to costly and time-consuming mistakes. Event-based traceability provides a robust method for handling both long-term evolutionary change as well as the short-term speculative change needed to support performance related impact analysis. By establishing dynamic links, capable of propagating data values and commands between requirements and performance models, it becomes possible to automate a wide range of speculative queries and to enhance the overall ability to predict the impact of change upon the performance of the system.

Software metrics increase our ability to understand the behavior of software systems. An accurate measurement provides us with solid understanding of the entity we are measuring. In Object Oriented software, most current metrics quantify a class's coupling complexity by simply counting the number of connections with other classes but such metrics are unable to capture the underlying complexity or tension of individual connections. In this paper we propose a technique for measuring the strength of interclass relationships that takes into account both the number of statements participating in the connection, as well as the complexity of those statements. Our approach introduces a new concept for measuring Object Oriented coupling complexity and provides a more sensitive measurement than traditional approaches. We present examples in C++ to support our method.

Because early design decisions can have a major long term impact on the performance of a system, early evaluation of the high-level architecture can be an important risk mitigation technique. This paper proposes a technique for predicting the volume of data that will flow across a network in a distributed system. The prediction is based upon anticipated execution of scenarios and can be applied at an extremely early stage of the design. It is driven by requirements specifications and captures dynamic metrics by defining typical usage patterns in terms of scenarios. Scenarios are then mapped to architectural components, and dataflow across inter-partition links is estimated. The feasibility of the approach is demonstrated through an experiment in which predicted metrics are compared to runtime measurements.

In the fall of 1998, the ACM Education Board and the Educational Activities Board of the IEEE Computer Society appointed representatives to a joint task force to prepare Computing Curricula 2001 (CC2001), the next installment in a series of reports on the undergraduate computer science curriculum that began in 1968 and was then updated in 1978 and 1991. Interim reports on the initial planning of the curriculum were presented at the SIGCSE symposium and the IEEE Frontiers in Education Conference in both 1999 and 2000. The CC2001 Task Force released its first draft report at the 2000 SIGCSE conference and plans to release its penultimate draft at SIGCSE 2001. The purpose of this session is to describe how we expect the recommendations of the report to apply in practice. The panelists represent a range of institutions and can therefore speak to the questions that audience members from similar institutions might have.

Function-Class Decomposition is a hybrid method that integrates structured analysis with an object oriented approach to decompose a system. The task of class identification is performed in parallel to the decomposition of the system into a hierarchy of functional modules. This hierarchy provides the infrastructure for a systematic approach to partitioning components for distribution and for evaluating key attributes of the resulting architecture. Complexity is reduced by the fact that partitioning decisions are made along the boundaries of previously identified groupings. Early evaluation of the resulting component distribution is also a key factor in mitigating the risks associated with developing distributed applications.

This paper presents effective algorithms for enumerating the minimal cutsets of networks. After a graph is modeled after a network, first, by a graph method the spanning tree of the graph and binary tree whose event is complement to it are evaluated. The sub-vertex set represented by the binary tree is referenced, and an algorithm which evaluates the minimal cutsets is proposed. This algorithm requires a space memorizing a topology of the graph, and has a time complexity which is proportional to a number of the edges of the graph and the binary tree. Second, by adding the branch, an algorithm enumerating the cutsets, which separates the sub-vertex set and a disjoint vertex set of the graph, is proposed. This algorithm does not enumerate all the events which the vertex and edge set can have, and executes computation to only the number of minimal cutsets. The space needed is proportional to the square of the number of the vertexes, and time complexity is in the order of the number of cutsets. The reliability is computed from the events of cutset.

In the fall of 1998, the ACM Education Board and the Educational Activities Board of the IEEE Computer Society appointed representatives to a joint task force to prepare Curriculum 2001, the next installment in a series of reports on the undergraduate computer science curriculum that began in 1968 and was then updated in 1978 and 1991. Interim reports on the initial planning of the curriculum were presented at the SIGCSE symposium in March 1999 and at the IEEE Frontiers in Education Conference in November 1999. In February 2000, the Curriculum 2001 Task Force will release a preliminary version of its report, in the hope of gaining feedback from a wider audience. The purpose of this panel is to give attenders at the SIGCSE conference to review the current state of the preliminary draft and offer their comments to the members of the Curriculum 2001 steering committee on the panel.

As a research based on technology integration, I/sup 3/ (pronounced "I cube") is an executable architectural component description language based on colored Petri-net semantics with the style and understandability of SADT. I/sup 3/ describes architectural components hierarchically in three levels. First, interface net declares services to be provided to other components. Next, interconnection net specifies the services to be acquired from other components. Finally, interoperation net describes the operational behavior of a component. Through its unique interconnection mechanism, I/sup 3/ facilitates the software architect in building an architectural model and formalizing the architectural topology and behavior of each software component as well as the entire system. Implementation of I/sup 3/ can be made CORBA-compliant.

Managing the development of large-scale software systems is a challenge to all software project managers due to the ever-increasing complexity inherent in the software development life-cycle. In this paper, a formalism intended to capture the concurrent and iterative nature of software development, called Software Project Management Net (SPMNet), is proposed in order to model software development projects. By augmenting and applying genetic algorithms in an innovative manner by sharply reducing the solution search-space complexity, SPMNet provides optimal or near-optimal solutions to the resource allocation and project scheduling problems. Results collected from simulation runs clearly support our approach for practical applications.

The authors present LTP (Language of Timed Processes), a process-algebra-based specification language, for the specification of real-time applications. In addition to timing requirements involving the execution time of individual computations, LTP can specify timing constraints that are associated with process descriptions: periodic constraints and sporadic constraints. Periodic processes (e.g. sensors) are usually time-driven; sporadic processes are usually event-driven. In LTP common real-time constraints such as delays, deadlines, and timeouts can be modeled conveniently. They specifically introduce a new construct for modeling periodic behavior, which is quite common in real-time applications. They validate LTP specifications by translating from LTP to a kind of timed automata that is amenable to well-known model checking techniques. Although the models of LTP specifications, commonly known as timed transition systems, are usually infinite because of the presence of time, the translation results in a finite representation by which automatic verification is possible.

In the real world, the popularity of each video is different. We propose a new popularity-based data allocation scheme to allocate data units within a cluster such that the corresponding data units of these popular videos are stored in those cylinders at one end of each cluster. Due to a higher spatial locality within these hot cylinders, some data units requested by the users are stored in the same cylinder such that one seek operation, one rotation, and one transfer operation are required to retrieve these data units. Therefore, the time required to retrieve data for these requests can be reduced, thus reducing the system response time as well. Based on our results, the system response time could be reduced by half or even more.

The paper gives a detailed description of the development of a distributed environment. A brief description of an object oriented technology used in the development of the environment is also presented. The environment described is suitable for rapid prototyping of distributed applications and complete development of distributed software systems. This environment was used in the development of client/server applications and the simulation of a cellular system. The cellular system featured Call-Handoff Cell-spitting and the dynamic allocation of cellular phones in the system.

A formalism, Software Project Management Net (SPMNet), is proposed to model the development of a software project. SPMNet provides a formal modeling technique to capture the concurrent and iterative nature of software development. A tracking mechanism in the SPMNet is used to keep track of all the events through the software development life cycle. Based on the previous-event information, the manager can make queries on the development history. In our approach, an automatic technique based on genetic algorithms is introduced to determine the optimal resource allocation and calculate the total time and cost of a project. Based on the information generated from genetic algorithms, the software manager will be able to predict the future states of a project by pre-executing the SPMNet.>

Well known for its features such as encapsulation and reusability object-oriented software development paradigm has been considered as a promising development approach which can help improve software quality and productivity. However, object-oriented design as not an easy task, and the design result hardly reveal functionalities to be provided by the system. In this paper, we propose a module-oriented design approach, which is based on a method termed class-function decomposition. This decomposition is not simply an object-oriented decomposition. More meaningfully, it takes a functional view into account. As a result, this approach will be more effective in supporting large and complex OO software development. In order to support this module oriented class-function design method, we also extend the semantic net to the semantic-condition model for more precise description of the design result.>

This paper proposes a new prototyping approach to software development to produce better-quality software more productively than traditional prototyping methodologies by combining formal methods and object-oriented techniques with prototyping concepts. In the prototyping process, specifications are gradually replaced with implementations which may be retrieved from libraries and refined afterwards. The executable specification language provides not only behavior/performance analysis but also simulation capabilities which are essential to prototyping. Additionally, a special mechanism called hybrid simulation takes it possible to simulate a mixture of specification and implementation, and serves to validate the intended system. Software reuse by object-orientation is one basis of the methodology, and eliminates the risk of producing a quick-and-dirty product, which is a typical prototyping problem. The seamlessness of object-orientation is indispensable to the methodology.>

In a heterogeneous database environment, accessing data across different types of data models requires an integration of conceptual schemas. This conceptual schema can exist in various forms such as relational, object-oriented, hierarchical, or network. This paper provides a mechanism, a mapping function - for the transformation of an object- oriented schema to a relational schema and a query translation to access an object-oriented database for the relational database user.>

The absence of satisfactory methods for verifying the liveness and fairness properties limits the analysis power of Petri net theories. An approach is introduced to connect the Petri net model with the Model Checker. A translator is used to transform the reachability graph of the Petri net to the Moore machine. The Moore machine and the behaviors specified by temporal logic are the inputs of the Model Checker, which is able to verify the properties of liveness and fairness. During the transformation, local and global behaviors of the Petri net model are separated, which means that a certain modularity can be achieved. An optimization technique is presented to trim the unnecessary local information from the local reachability graphs. The space complexity of manipulating the global reachability graph, which is generated by combing the trimmed local reachability graph, can be reduced. Moreover, a new approach is proposed to verify the concurrency behavior by using the Model Checker.>

A specification-based testing method aimed primarily at detecting errors of distributed systems is presented. This specification technique emphasizes the communication behaviors and the synchronization properties of distributed processes. The functions of behavior specification for the testing method serve two purposes. First, it provides the communication-oriented high-level view of the system, which dictates the method of test case generation. Five test criteria peculiar to distributed software are considered. Test data can be derived from the source code based on the symbolic execution technique applied to each test case. Second, the specification serves as a correct reference model, which helps analyze the communication events of the system execution. Currently, this testing method, as an important support to the INTEGRAL verification and validation system, has been largely realized in a SUN workstation environment.>

A binary coding method was invented and has been applied to the single-key-lock (SKL) system which achieves access control by associating with each accesser only one key and with each resource only one lock. The new system is called the binary single-key-lock (BSKL) system. In both the SKL and BSKL systems, through operations on the single-key-lock pair, the control information can be revealed. On the basis of the new binary coding method, and because binary operations are much faster, better performance for such an access control system can be achieved. The increased extensibility of the modification is also discussed.>

The authors have designed a system functional requirement (SFR) specification processor using a knowledge-based system approach in conjunction with other artificial intelligence techniques, this processor can be used for a variety of software systems. Domain-specific expert knowledge is defined for each software system application. The adopted specification language can easily be read by a variety of people such as customers, managers, requirement writers, software designers, and the test team. The processor, for a given software system application, not only checks for consistency, completeness, nonredundancy, and nonambiguity, but also uses domain-specific expert knowledge to verify that the SFR specification document is correct from the domain expert's point of view. To illustrate the proposed approach, the authors give an example of a component of a telephone switching system and the processor system methodology.>