Requirements elicitation is the most crucial yet challenging activity in the requirements analysis phase.Several studies conducted in this field aid in this activity; however, requirements elicitation for innovative systems remains a difficult task due to the lack of experts for such novel systems.To address this challenge, we discuss a requirements elicitation process based on collaborative LLMs.This process involves multiple LLMs, each representing various experts, to gather a diverse range of opinions.We conducted two preliminary case studies aimed at collecting opinions about innovative systems by broadening the pool of experts.The results indicate that this process systematically increases the number of collected opinions.We will discuss our future plans, drawing insights from the results of these preliminary case studies.

Reinforcement learning is a powerful methodology that enables self-adaptive systems to relearn and update their adaptation policy when dealing with unforeseen changes. To update the policy more efficiently, several knowledge reuse approaches have been proposed to speed up relearning. However, the current studies treat and reuse the knowledge integrally, which may result in increased relearning costs if the reused knowledge is inappropriate in the changed situation. Generally, some localized pieces of the knowledge are still appropriate for reuse if they are not related to the changes, while some pieces may become inappropriate for reuse if they are affected by the changes. This paper proposes a goal-oriented curriculum evolution method to realize finer-grained knowledge reuse, combining goal-oriented modeling and curriculum learning. The method is twofold: (1) at design time, we apply goal-oriented modeling to design a curriculum in which an RL problem is decomposed into sub-problems, so that knowledge can be decomposed into several pieces of localized knowledge for sub-problems, and (2) at runtime, we evolve the curriculum to reflect changes (i.e., update the sub-problems related to the changes), so that the affected pieces of knowledge can be locally updated to make them appropriate for reuse in the changed situation. The evaluation based on a cleaning robot shows that the relearning time was shortened, demonstrating the effectiveness of our method.

The Smart Systems and Services Innovative Professional Education (Smart SE) program is a certification program developed as part of the education network for the Practical information Technologies (enPiT-Pro) project, which is funded by the Japan Ministry of Education, Culture, Sports, Science and Technology. The Smart SE program provides industry professionals working in fields related to information and communication technology (ICT) with additional training and education in smart systems and services that utilize various technologies such as IoT, Cloud, Big Data, and Artificial Intelligence (AI) for businesses. Here, we illustrate its purpose, curriculum and features to respond to the needs of industrial professional education.

In event-based systems, safety properties are critical requirements to prevent the system from bad things happen. However, safety properties may be violated because of the runtime system functional fault. From the viewpoint of a self-adaptive system, such a system should be requirement-aware and changes its behavior to satisfy the designed requirements as much as possible. The previous work proposed a method to analyze possible adaptation options with degrading different requirements. Here, we propose an efficient difference analysis algorithm to shorten the analysis time so that the adaptation to functional fault can be more timely. Our idea is to reuse the analysis result of development time and re-analyze the changed part only, instead of performing the complete analysis from scratch. We evaluated our algorithm's efficiency based on three case studies: a coalmine pump-control system, a cyber-physical security people-flow restriction system, and a factory production cell system. The experiment results indicate that our algorithm averagely reduces 75.9% of analysis time compared with the existing analysis technique.

Running applications for one operating system (OS) on another OS is useful in many cases. However, porting applications requires high development costs and running applications in a virtual machine poses resource sharing problems. A promising approach is to use an OS compatibility layer that converts the guest application's interface into the host one. Unfortunately, existing OS compatibility layers sacrifice either robustness or flexibility due to in-kernel subsystems or user-space-only implementations. This paper proposes a new architecture of OS compatibility layers that achieves robustness with almost user-level implementations while improving flexibility by exploiting standardized virtualization interfaces supported by most modern OSs. Our implementation of a Linux compatibility layer for macOS called Noah can run many of Ubuntu Linux binary applications, and a prototype implementation of a Linux compatibility layer for Windows confirmed the generality of our approach. Our experimental results demonstrated that the overhead of Linux kernel build time on Noah was 16%.

Discrete event controllers are at the heart of many software systems that require continuous operation. Changing these controllers at runtime to cope with changes in its execution environment or system requirements change is a challenging open problem. In this paper we address the problem of dynamic update of controllers in reactive systems. We present a general approach to specifying correctness criteria for dynamic update and a technique for automatically computing a controller that handles the transition from the old to the new specification, assuring that the system will reach a state in which such a transition can correctly occur and in which the underlying system architecture can reconfigure. Our solution uses discrete event controller synthesis to automatically build a controller that guarantees both progress towards update and safe update.

Abstract Event-B has been attracting much interest because it supports a flexible refinement mechanism that reduces the complexity of constructing and verifying models of complicated target systems by taking into account multiple abstraction layers of the models. Although most previous studies on Event-B focused on model construction, the constructed models need to be maintained. Moreover, parts of existing models are often reused to construct other models. In this paper, a method is introduced that improves the maintainability and reusability of existing Event-B models. It automatically reconstructs the refinement structure of existing models by constructing models about different sets of variables than that used in the original models, while maintaining the consistencies checked in the original models. The method automatically decomposes each refinement step into multiple steps by taking certain predicates from existing models and deriving additional predicates from the consistency conditions of existing models to create new models consistent with the original ones. By combining the decomposing of refinement steps with the composing of refinement steps, this method automatically restructures a refinement step in accordance with given sets of variables to be taken into account in refinement steps of the refactored models. The results of case studies in which large refinement steps in existing models were decomposed and existing models were restructured to extract reusable parts for constructing other models demonstrated that the proposed method facilitates effective use of the refinement mechanism of Event-B.

Event-B requires engineers to satisfy proof obligations and inherit all predicates from abstract models while constructing concrete ones. Engineers typically derive predicates from abstract models through transformation with the intention of gradually refining the models. These kinds of intentions for refinement are essential for other engineers to understand the refinements. However, these are implicit and not directly specified in the models. Therefore, it is difficult to understand how each predicate in concrete models is obtained from the predicates in abstract models. This paper proposes an effective method of extracting these relationships. Our approach uses heuristics to avoid exhaustive matching between predicates. It tries to find a set of related predicates by tracing elements, such as variables and constants in predicates, and excluding predicates that use common variables, but differently. Our method facilitates understanding of the intentions of refinements and can be used to help reverse engineering by clarifying the relationships between predicates through refinements.

Mutation analysis is a method for evaluating the effectiveness of a test suite by seeding faults artificially and measuring the fraction of seeded faults detected by the test suite. The major limitation of mutation analysis is its lengthy execution time because it involves generating, compiling and running large numbers of mutated programs, called mutants. Our tool MuVM achieves a significant runtime improvement by performing higher order mutation analysis using four techniques, meta mutation, mutation on virtual machine, higher order split-stream execution, and online adaptation technique. In order to obtain the same behavior as mutating the source code directly, meta mutation preserves the mutation location information which may potentially be lost during bit code compilation and optimization. Mutation on a virtual machine reduces the compilation and testing cost by compiling a program once and invoking a process once. Higher order split-stream execution also reduces the testing cost by executing common parts of the mutants together and splitting the execution at a seeded fault. Online adaptation technique reduces the number of generated mutants by omitting infeasible mutants. Our comparative experiments indicate that our tool is significantly superior to an existing tool, an existing technique (mutation schema generation), and no-split-stream execution in higher order mutation.

Service composition provides a means of customized and flexible integration of service functionalities. Quality-of-service (QoS) optimization algorithms select services to adapt workflows to the non-functional requirements of the user. With increasing number of services in a workflow, previous approaches fail to achieve a sufficient reliability. Moreover, expensive ad-hoc replanning is required to deal with service failures. The major problem with such sequential application of planning and replanning is that it ignores the potential costs during the initial planning and they consequently are hidden from the decision maker. Our idea to overcome this problem is to compute a QoS optimized selection of service clusters that includes a sufficient number of backup services for each service employed. These backup services should be sufficiently distributed to prevent a task failure in case of, e.g., a network failure. To support the decision maker in the selection task, our multi-objective approach considers the possible repair costs directly in the initial composition. Our graphical user interface visualizes the resulting QoS of the workflow and the location of the services to enable the decision maker to select compositions in line with risk preferences. We prove the benefits of our approach in our detailed evaluation.

Feedback-directed random test generation is a widely used technique to generate random method sequences. It leverages feedback to guide generation. However, the validity of feedback guidance has not been challenged yet. In this paper, we investigate the characteristics of feedback-directed random test generation and propose a method that exploits the obtained knowledge that excessive feedback limits the diversity of tests. First, we show that the feedback loop of feedback-directed generation algorithm is a positive feedback loop and amplifies the bias that emerges in the candidate value pool. This over-directs the generation and limits the diversity of generated tests. Thus, limiting the amount of feedback can improve diversity and effectiveness of generated tests. Second, we propose a method named feedback-controlled random test generation, which aggressively controls the feedback in order to promote diversity of generated tests. Experiments on eight different, real-world application libraries indicate that our method increases branch coverage by 78% to 204% over the original feedback-directed algorithm on large-scale utility libraries.

Mobile applications (apps) are software developed for use on mobile devices and made available through app stores. App stores are highly competitive markets where developers need to cater to a large number of users spanning multiple countries. This work hypothesizes that there exist country differences in mobile app user behavior and conducts one of the largest surveys to date of app users across the world, in order to identify the precise nature of those differences. The survey investigated user adoption of the app store concept, app needs, and rationale for selecting or abandoning an app. We collected data from more than 15 countries, including USA, China, Japan, Germany, France, Brazil, United Kingdom, Italy, Russia, India, Canada, Spain, Australia, Mexico, and South Korea. Analysis of data provided by 4,824 participants showed significant differences in app user behaviors across countries, for example users from USA are more likely to download medical apps, users from the United Kingdom and Canada are more likely to be influenced by price, users from Japan and Australia are less likely to rate apps. Analysis of the results revealed new challenges to market-driven software engineering related to packaging requirements, feature space, quality expectations, app store dependency, price sensitivity, and ecosystem effect.

To meet future demands for wireless sensor network (WSN) software, both experts and average software developers should be involved in WSN software development. However, WSN software development is difficult for the average software developer because data processing-related design and network-related design are tangled in the software. Here, we propose a software development process for WSN software by stepwise refinement. Our process enables stepwise refinement to separately address data processing-related and network-related concerns, reuse of well-defined designs, and implementations for network-related concerns prepared by the experts, and perform model-driven development to obtain source codes from models by model transformations. Additionally, we used case studies using actual WSN software development and user studies to evaluate how our proposed process can support actual WSN software development.

Virtualization techniques greatly benefit cloud computing. Live migration enables a datacenter to dynamically replace virtual machines (VMs) without disrupting services running on them. Efficient live migration is the key to improve the energy efficiency and resource utilization of a datacenter through dynamic placement of VMs. Recent studies have achieved efficient live migration by deleting the page cache of the guest OS to shrink the memory size of it before a migration. However, these studies do not solve the problem of IO performance penalty after a migration due to the loss of page cache. We propose an advanced memory transfer mechanism for live migration, which skips transferring the page cache to shorten total migration time while restoring it transparently from the guest OS via the SAN to prevent IO performance penalty. To start a migration, our mechanism collects the mapping information between page cache and disk blocks. During a migration, the source host skips transferring the page cache but transfers other memory content, while the destination host transfers the same data as the page cache from the disk blocks via the SAN. Experiments with web server and database workloads showed that our mechanism reduced total migration time with significantly small IO performance penalty.

Energy efficiency of cloud data centers is of great concern today and has been tackled by many researchers. Dynamic VM placement is a well-known strategy to improve energy efficiency of a data center. Virtual machines (VMs) under light load are consolidated into a small number of physical machines (PMs) to turn idle PMs into low-power states. Although live migration is essential for dynamic VM placement, former studies have not yet revealed how energy overhead of live migration has impact on energy efficiency of dynamic VM placement. To tackle this problem, we conducted integrated simulation of energy overhead of live migration and dynamic VM placement sing Sim Grid. We used three dynamic VM placement policies and two live migration mechanisms (existing pre-copy and an accelerated mechanism invented by us) to thoroughly evaluate the energy overhead. The results showed that in the worst case energy overhead of live migration occupies 5.8% of total energy consumption of a data center.

Method names are important for the software development process. It has been shown by some studies that the quality of method names affects software comprehension. In response, some approaches that evaluate comprehensibility of method names have been proposed. However, the effectiveness of existing approaches is limited because they focus on part of names.

The challenge of validating Asynchronous JavaScript and XML (Ajax) applications lies in actual errors exposed in a user environment. Several studies have proposed effective and efficient testing techniques to identify executable faults. However, the applications might have faults that are not executed during testing, but might cause actual errors in a user environment. Although we have investigated static methods for finding ``potential faults'' that seem to cause actual errors if executed, developers need to confirm whether or not the potential faults are actually executable. Herein, we propose a mutation-based testing method implemented in a tool called JSPreventer. Even if the potential faults are not easily executable in a given environment, our method mutates the applications until they are executable using two delay-based mutation operators to manipulate the timing of the applications handling interactions. Thus, JSPreventer provides executable evidences of the not-easily-executable faults for developers, if it reveals actual errors by testing the mutated applications. We applied our method to real-world applications and found actual errors that developers could debug to improve their reliability. Therefore, JSPreventer can help developers validate reliable real-world Ajax applications.

Wireless sensor networks (WSNs) play important roles in systems that interact with the real world (e.g., cyber-physical systems and internet of things). To mitigate the complex nature of WSNs, many platforms at different abstraction levels (e.g., abstraction of hardware, communications, and individual nodes) have been proposed in the last decade. WSNs in different environments might employ different platforms to satisfy nonfunctional requirements (NFRs) because the diversity of platforms results in trade-offs of NFRs and the quality of WSN heavily depends on the environment it runs. Although WSN software should be highly portable, existing approaches only support porting between operating systems and not more abstract middleware. Herein we propose a methodology with a framework to capture various platforms in the WSN domain with platform-independent multi-level models. A case study demonstrates that our framework can port WSN software to platforms at different abstraction levels and adapt the software to the new platform to improve performance.

Service-Oriented Computing enables the composition of loosely coupled services provided with varying Quality of Service (QoS) levels. Selecting a near-optimal set of services for a composition in terms of QoS is crucial when many functionally equivalent services are available. As the number of distributed services, particularly in the cloud, is rising rapidly, the impact of the network on the QoS keeps increasing. Despite this, current approaches do not differentiate between the QoS of services themselves and the network. Therefore, the computed latency differs from the actual latency, resulting in suboptimal QoS. Thus, we propose a network-aware approach that handles the QoS of services and the QoS of the network independently. First, we build a network model in order to estimate the network latency between arbitrary services and potential users. Our selection algorithm then leverages this model to find compositions with a low latency for a given execution policy. We employ a self-adaptive genetic algorithm which balances the optimization of latency and other QoS as needed and improves the convergence speed. In our evaluation, we show that our approach works under realistic network conditions, efficiently computing compositions with much lower latency and otherwise equivalent QoS compared to current approaches.

Live migration of virtual machines over a wide area network has many use cases such as cross-data center load balancing, low carbon virtual private clouds, and disaster recovery of IT systems. An efficient wide area live migration method is required because cross-data center connections have a narrow bandwidth. Page cache occupies a large portion of the memory of a Virtual Machine (VM) when it executes data-intensive workloads. We propose a new live migration technique, page cache teleportation, which reduces the total migration time of wide area live migration and has a low overhead. It detects the restorable page cache in the guest memory that has the same contents as the corresponding disk blocks. The restorable page cache is not transferred via the WAN but is restored from the disk image before the VM resumes. In this way, the IO performance degradation reduces after the migration. Evaluations show that page cache teleportation reduces the total migration time of wide area live migration and has a lower performance overhead than existing approaches.

In reinforcement learning tasks with continuous state-action, parameterized policy search has been known to be a powerful method. Applying NeuroEvolution (NE) to optimizing the policy represented by artificial neural network (ANN) is a particularly active research field. In most cases, NE algorithms cost a large amount of trial-and-error (episode) to optimize policies. However, due to time and cost constraints, researchers and practitioners cannot repeat a number of episodes on physical robots. Thus, choosing an efficient NE algorithm is a key to optimize policies with limited time and cost. In this work, our goal is to help users to choose an efficient NE algorithm. We compare and analyze sample efficiency of two successful state-of-the-art NE algorithms: CMA-NeuroES and NEAT in a gait generation task of a quadruped robot. Moreover, we run both algorithms with various initial topologies in order to analyze the performance difference between each topology. From experimental results, we show CMA-NeuroES outperforms NEAT regardless of initial topologies when the limited number of episodes can be executed. Additional experiments conclude that the optimization method for connection weights in NEAT results in its inferior performance to CMA-NeuroES, while a probability-weighted averaging characteristic and self-adaptive factors make CMA-NeuroES to be advantageous.

Summary form only given. Software evolution is an essential activity that adapts existing software to changes in requirements. It has played a central role in the overall software lifecycle in recent years. It is generally acknowledged that the software employed in real-world environments must continuously evolve and adapt. Many studies, on the other hand, have suggested that software evolution consumes a large part of development costs. Localizing the impact of changes is one of the most efficient strategies for successful evolution. It helps us to correctly estimate the impact of changes on systems and make changes that minimize increases in code complexity. Both of these activities, i.e., impact analysis and change implementation, have been recognized as integral to software evolution. However, it is still difficult to predict the impact of software changes even if the developers are experienced. Therefore, a software development process should help developers correctly analyze the impact of changes. Moreover, having to make continuous changes to a piece of code easily makes it complicated. Therefore, the process should also help developers to change systems while preventing code from becoming too complicated. We exploit requirements descriptions in order to extract loosely coupled components and localize changes for evolution. We define a process of elaboration for the goal model that extracts a set of control loops from the requirements descriptions as components that constitute extensible systems [1]. A control loop is a cyclic activity flow based on the process control model, which is a model for defining system behaviors. A controller in the model forms a (control) loop consisting of four key activities: collect, analyze, decide, and act. This cycle starts with a collection of relevant data that reflects the current state of the system. The system analyzes the collected data and adjusts its behavior to achieve its goals. After that, the system acts in a way that reflects decisions. We regard such control loops to be independent components that prevent the impact of a change from spreading outside them. When additional requirements appear, new control loops are easy to embed into a system constructed with multiple control loops. Even if the requirements change, the idea of compositional control loops can easily accommodate these changes simply by updating the relevant control loops. We devised a goal model compiler [2] to enable partial design models to be extracted for each evolution and built a programming framework [3] that enables execution of multiple control loops. The goal model compiler identifies multiple control loops from goal-oriented requirements descriptions. We experimentally evaluated our approach in two types of software development and the results demonstrate that our elaboration technique helps us to analyze the impact of changes in the source code and prevent the complexity of the code from increasing.

When developing asynchronous JavaScript and XML (Ajax) applications, developers implement Ajax design patterns for increasing the usability of the applications. However, unpredictable contexts of running applications might conceal faults that will break the design patterns, which decreases usability. We propose a support tool called JSVerifier that auto-matically verifies interaction invariants; the applications handle their interactions in invariant occurrence and order. We also present a selective set of interaction invariants derived from Ajax design patterns, as input. If the application behavior breaks the design patterns, JSVerifier automatically outputs faulty execution paths for debugging. The results of our case studies show that JSVerifier can verify the interaction invariants in a feasible amount of time, and we conclude that it can help developers increase the usability of Ajax applications.

Software evolution is an essential activity that adapts existing software to changes in requirements. Localizing the impact of changes is one of the most efficient strategies for successful evolution. We exploit requirements descriptions in order to extract loosely coupled components and localize changes for evolution. We define a process of elaboration for the goal model that extracts a set of control loops from the requirements descriptions as components that constitute extensible systems. We regard control loops to be independent components that prevent the impact of a change from spreading outside them. To support the elaboration, we introduce two patterns: one to extract control loops from the goal model and another to detect possible conflicts between control loops. We experimentally evaluated our approach in two types of software development and the results demonstrate that our elaboration technique helps us to analyze the impact of changes in the source code and prevent the complexity of the code from increasing.

In Infrastructure-as-a-Service datacenters, the placement of Virtual Machines (VMs) on physical hosts are dynamically optimized in response to resource utilization of the hosts. However, existing live migration techniques, used to move VMs between hosts, need to involve large data transfer and prevents dynamic consolidation systems from optimizing VM placements efficiently. In this paper, we propose a technique called “memory reusing” that reduces the amount of transferred memory of live migration. When a VM migrates to another host, the memory image of the VM is kept in the source host. When the VM migrates back to the original host later, the kept memory image will be “reused”, i.e. memory pages which are identical to the kept pages will not be transferred. We implemented a system named MiyakoDori that uses memory reusing in live migrations. Evaluations show that MiyakoDori significantly reduced the amount of transferred memory of live migrations and reduced 87% of unnecessary energy consumption when integrated with our dynamic VM consolidation system.

Neuroevolutionary algorithms are successful methods for optimizing neural networks, especially for learning a neural policy (controller) in reinforcement learning tasks. Their significant advantage over gradient-based algorithms is the capability to search network topology as well as connection weights. However, state-of-the-art topology evolving methods are known to be inefficient compared to weight evolving methods with an appropriately hand-tuned topology. This paper introduces a novel efficient algorithm called CMA-TWEANN for evolving both topology and weights. Its high efficiency is achieved by introducing efficient topological mutation operators and integrating a state-of-the-art function optimization algorithm for weight optimization. Experiments on benchmark reinforcement learning tasks demonstrate that CMA-TWEANN solves tasks significantly faster than existing topology evolving methods. Furthermore, it outperforms weight evolving techniques even when they are equipped with a hand-tuned topology. Additional experiments reveal how and why CMA-TWEANN is the best performing weight evolving method.

Service composition algorithms are used for realizing loosely coupled interactions in Service-Oriented Computing. Starting from an abstract workflow, concrete services are matched, based on their QoS, with the preferences and constraints of users. Current approaches usually only consider static QoS values and find a single solution consisting of one concrete service for each workflow task. In a business-to-business (B2B) environment, though, there are additional requirements for service compositions: 1) a high number of invocations, and 2) a high reliability. Thus, we introduce a probabilistic approach on the basis of a new QoS model to solve the composition problem for such long-term B2B service compositions. For each task and for every point in time, we determine the most appropriate services and backup services for a specific user. Thus, the selection depends on the actual response time and reliability, or recent invocation failures or timeouts. For that purpose, we propose an adaptive genetic algorithm that employs our QoS model and determines backup services dynamically based on the required reliability. Our evaluations show that our approach significantly increases the utility of long-term compositions compared with standard approaches in the envisioned B2B environments.

The concept of trust in web services mainly deals with the degree of belief that a client or a group of clients have over services functioning satisfactorily and providing the expected results. With services being invoked in composition with each other, computing the trust of the composition and selecting services that deliver the highest trust for it becomes a desired goal. In this paper we demonstrate how using Bayesian networks and its supporting queries, we can select the set of services among all candidates that would provide highest global trust.

Optimizing the Quality-of-Service (QoS) levels of a service workflow is essential for the user satisfaction in Service-oriented Computing. For that purpose, QoS computation models are applied to reflect the actual QoS experienced by the user during service execution. Current QoS models ignore the possible dependencies of QoS attributes, such as the dependency on the time of the execution or on the input data supplied to the service. Apart from that, composition approaches consider only single workflows during service selection, narrowing the number of possible compositions. Thus, we introduce a novel QoS model that covers QoS dependencies and discuss how this model can be used to consider multiple workflows at the same time. Moreover, we adopt a multi-objective optimization approach to offer solutions varying in QoS such as finishing time and price, allowing the user to make fine-grained decisions.

The technology for web services has facilitated composition of a new service by combining existing services. The resulting composite service is required to guarantee Quality of Service (QoS), such as price, in addition to the demanded function. Each composite service faces computationally-expensive service selection by exploring enormous service combinations for optimization of a utility function and satisfaction of global constraints. In addition, each concrete service has difficulties in determining QoS that is profitable while attractive for customers, or composite services. Specifically, information on rival services is generally secret and changeable, which is required for the optimal decision. In response to these problems, this paper proposes an algorithm for service selection using Vickrey auction. In the first phase of the proposed algorithm, the composite service selects some best concrete services through the reverse Vickrey auction, only considering the utility function. In the second phase, the global constraints are satisfied through adjustments based on the Vickrey auction. The proposed algorithm avoids full exploration of services combinations for efficient computation, while encouraging providers to declare their best QoS without caring about extra profit.

Service-Oriented Computing (SOC) enables the composition of loosely coupled services provided with varying Quality of Service (QoS) levels. Selecting a (near-)optimal set of services for a composition in terms of QoS is crucial when many functionally equivalent services are available. With the advent of Cloud Computing, both the number of such services and their distribution across the network are rising rapidly, increasing the impact of the network on the QoS of such compositions. Despite this, current approaches do not differentiate between the QoS of services themselves and the QoS of the network. Therefore, the computed latency differs substantially from the actual latency, resulting in suboptimal QoS for service compositions in the cloud. Thus, we propose a network-aware approach that handles the QoS of services and the QoS of the network independently. First, we build a network model in order to estimate the network latency between arbitrary services and potential users. Our selection algorithm then leverages this model to find compositions that will result in a low latency given an employed execution policy. In our evaluation, we show that our approach efficiently computes compositions with much lower latency than current approaches.

Web service composition provides a means of customized and flexible integration of service functionalities. Quality-of-Service (QoS) optimization algorithms select services in order to adapt workflows to the non-functional requirements of the user. With increasing number of services in a workflow, previous approaches fail to achieve a sufficient reliability. Moreover, expensive ad-hoc replanning is required to deal with service failures. The major problem with such sequential application of planning and replanning is that it ignores the potential costs during the initial planning and they consequently are hidden from the decision maker. Our basic idea to overcome this substantial problem is to compute a QoS optimized selection of service clusters that includes a sufficient number of backup services for each service employed. To support the human decision maker in the service selection task, our approach considers the possible repair costs directly in the initial composition. On the basis of a multi-objective approach and using a suitable service selection interface, the decision maker can select compositions in line with his/her personal risk preferences.

Self-optimizing mechatronic systems are a new class of technical system promising new levels of flexibility and utility in electro-mechanical systems. Planning is an important method to realize self-optimization, although today hardly used in mechatronics. In this context, planning is understood as search for a feasible sequence of operations which implements the execution of specific job assigned to a system. This search is a complex and time-consuming task. Hence, it is desirable to decompose the planning task into smaller sub problems according to paradigm of divide & conquer and use problem specific solution methods. Unfortunately, possible planning sub problems in mechatronic systems cannot be considered isolated since sub modules influence each other. This paper introduces the application of a multi-agent-planning model based on cooperative objective functions that enable the coordinated solution of sub problems.

Adaptive and service-oriented manufacturing proposes manufacturing systems which can be rapidly changed in accordance to changing production programs and customer individual production. Increasing customer specific manufacturing and new trends such as Sustainable Manufacturing create a complex system of objectives. Human dispatcher controlling these manufacturing systems must be properly supported to consider all required objectives. Due to the reduced insight into the adapting manufacturing systems, classical single optimization approaches with a rigid definition of a single objective function and hard optimization constraints are not appropriate any more. This paper introduces a decision making framework based on multi-objective scheduling. The decision making process consists of a multi-objective scheduling process and an interface for schedule selection. The interface combines the decision making approaches outranking and preference elicitation.

A "shared" wireless sensor network (WSN) where multiple users deploy multiple tasks requires a mechanism for resource arbitration. This arbitration can be viewed as a problem with determining how best to assign sensors to tasks. The previous approaches only dealt with energy used for sensing, however, experiments have shown that the energy used for communication must be dealt with within the context of a WSN. In this paper, we propose a novel formulation of this problem and a distributed assignment algorithm based on heuristics that takes into account path information between the assigned node and the base station. We take advantage of the "reactive" nature of the system and piggyback path information on task queries. We can expect higher utilization of the network in comparison with previous approaches by preserving routes through which sensing data run. The results of software simulations revealed that our assignment algorithm is more energy-efficient than the previous one that does not exploit information about the path.

Due to the increase in Web services, many recent studies have been addressing the service selection problem based on non-functional or quality aspects. Our study incorporates combinational use of functionally-equivalent services into the problem to compose an application of higher quality or with additional value. However, when such combinational use is introduced, computational cost for the service selection becomes much higher. In this work, we propose a set of methods that reduce the additional cost for the QoS (Quality of Service)-based service selection considering combinational use. This approach achieves low cost by considering only effective combinations. The experimental results show that it can reduce computational cost regardless of the number of services and whatever their QoS values are while keeping the effectiveness of combinational use.

Service-Oriented Architecture enables the composition of loosely coupled services provided with varying Quality of Service (QoS) levels. Given a composition, finding the set of services that optimizes some QoS attributes under given QoS constraints has been shown to be NP-hard. Therefore, heuristic algorithms are widely used, finding acceptable solutions in polynomial time. Still the time complexity of such algorithms can be prohibitive for real-time use, especially if the algorithms are required to run until they find near-optimal solutions. Thus, we propose a heuristic approach based on Hill-Climbing that makes effective use of an initial bias computed with Linear Programming, and works on a reduced search space. In our evaluation, we show that our approach finds near-optimal solutions and achieves a low time complexity.

With the steadily growing number of service providers the competition becomes more and more intense. In order to find a distinctive edge over other competitors, automatic service composition can be applied to further adapt to the requirements of the users. Most of the current composition approaches can be categorized as either planning or selection algorithms. The former automatically create workflows and tend to neglect Quality-of-Service (QoS) concerns, drawing compositions useless if user constraints are violated. Selection algorithms on the other hand optimize the QoS but provide no functional flexibility. In this paper we show how the strengths of both approaches can be combined by leveraging common characteristics of service registries. Therefore we utilize a data structure which arranges functionally similar services in clusters and computes the QoS of each cluster. Our planning tool composes workflows consisting of these clusters, taking the QoS of the clusters into account. This way, the utility in general and the reliability of the composed workflows are significantly increased. We prove the feasibility and the benefits of our functional clustering in our detailed evaluation.

A wireless sensor network (WSN) consists of spatially distributed nodes that monitor physical conditions. Thus far, WSNs have been constructed for specific applications. However, in the future, system operators may want an existing WSN to deploy simultaneous applications. Existing studies have demonstrated the effectiveness of applying mobile agents to enable simultaneous applications in WSNs. However, two problems make the present form of mobile agents impractical for applications involving cooperation and group migration by mobile agents. The first problem is the lack of a suitable architecture enabling cooperation and interaction between agents, and the second is the lack of an efficient way of achieving group migration by agents in WSNs. To address these problems, we propose an architecture organized by mobile agents that has specific functionalities and that enables group migration by multiple agents.

Geographic information systems use databases to map keywords to places. These databases are currently most often created by using a top-down approach based on the geographic definitions. However, there is a problem with this approach in that these databases only contain location definitions such as addresses and place names, which does not allow for searches using keywords other than these words. Additionally, they do not give any information on the popularity, e.g., which is more popular among the places indexed by the same keyword. A bottom-up approach, based on the actual usage of words, can address these problems. We propose a method to aggregate tagging data and extract places related to a tag using the pair of a tag and a geo-tagged photo. We target the co-occurrence of a tag and the geolocation and represent the places related to a tag as a probability distribution over the longitudes and latitudes. We applied our method to data on the photo sharing service Flickr and experimentally confirmed that our method made it possible to highly-accurately extract places related to tags. Our direct bottom-up approach enables the extraction of place information that is not obtained by using traditional top-down approaches.

Niching schemes, which sustains population diversity and let an evolutionary population avoid premature convergence, have been extensively studied in the research field of evolutionary algorithms. Neuroevolutionary (NE) algorithms, such as NEAT, have also benefitted from niching. However, the latest research indicates that the use of genotype- or phenotype-similarity-based niching schemes in NE algorithms is not highly effective because these schemes have difficulty sustaining the behavioral diversity in the environment. In this paper, we propose a novel niching scheme that takes into consideration both the phenotypic and behavioral diversity, and then integrate it with NEAT. An experimental analysis revealed that the proposed algorithm outperforms the original NEAT for various problem settings. More interestingly, it performs especially well for problems with a high noise level and large state space. Since these features are common in problems to which NEAT is applied, the proposed algorithm should be effective in practice.

Geographic routing protocols exploit node positions on the Euclidean plane to route packets in multi-hop networks. Their potential lies in forgoing the overhead of building routing structures for instance in the form of link-state tables. At the same time their performance suffers from local dead ends and missing or inaccurate node coordinates. These issues have been addressed by various means, most notably by falling back to face routing on a planarized network graph or to a backup routing infrastructure such as a minimum spanning tree. Existing solutions however are not geared towards finding efficient paths or result in a considerable -- often upfront and global -- communication overhead.

The Business Process Execution Language (BPEL) has become the standard in Web service composition, so that been adopted by industries to create mission critical services. The business process reliability especially business process consistency when some services failed is critical to composite services especially involving various organizations. However, it is difficult to conduct business process reliability testing for composite services due to complex business process in service composition, remote deployed services and long time testing duration. Little research is done to test fault tolerance capabilities of composite services only from low level. This paper proposes a technique on how to test the reliability of composite service defined in BPEL from the view of business semantics with little cost using fault injection. We present an approach for reliability testing of web services by using service stubs with semantic faults instead of real services which can be placed at service provider side or service consumer side. Dislike existing studies, we focus on how to find the reliability problems associating with business process - called semantics as the problems are not pure coding error but faults related to business process. In addition, the behavior of composite services in BPEL is analyzed when there are faults in orchestrated services invoked. Finally, a case study is given to show the whole process of reliability testing for composite web service.

Distributed hash tables (DHT) are used for data management in P2P environments. However, since most hash functions ignore relations between items, DHTs are not efficient for operations on related items. In this paper, we modify a DHT into a distributed array (DA) that enables efficient operations on logical arrays. The array elements of a DA are placed in a P2P overlay network according to a simple rule such that the load is balanced and the number of messages required to access elements sequentially is reduced. The number of messages required for array operations is much smaller than that for operations on DHTs. We demonstrate this theoretically and experimentally.

Considerable research has been done on different aspects of sensor networks. However management issues for these devices are still little explored. Nonetheless, with the increasing number of heterogeneous distributed sensors in various application domains, their management gains more and more importance, in particular for domains where requirements in terms of quality service, reliability, security and integrity are high, e.g., industrial, medical and domotics. This paper promotes an integrated management solution that covers domains such as network, system, application and device management. It first gives a brief state of the art of management solutions for traditional computer systems and sensor networks, then from a device management point of view, it presents challenges and possible solutions for generic networked sensing device management.

Formal methods allow to verify several properties of specifications and implementations. Intra-specification consistency means that a specification does not contradict itself. When specifications evolve over time, one also wants to check inter-specification consistencies, which mean that specifications defined earlier in the development cycle also hold at a later point in time. VDM++ is a popular and easy-to-use formal specification language. It uses testing instead of formal proofs to validate the consistency of specifications. The strictness of validations thus depends on the completeness of the corresponding test suites. Unfortunately, VDM++ does not support the verification of inter-specification consistencies. We define VDM-R, an extension of VDM++, which allows to annotate relationships between specifications. We also provide the tool VR2EvtB to translate from VDM-R to Event-B. Using an Event-B verifier, we can then formally validate intra- and inter-specification consistencies in an almost fully-automated process.

Model checking is a powerful technique for verifying the correctness of a systempsilas specification. But even when the specification has been verified to be correct, there is still the question of whether the specification covers all the expected behaviors. One of the most important issues for verification is the sufficiency of verification items. In model checking, specification-level properties such as reachability are well-studied, but the sufficiency of a specification against the preceding requirements still remains a challenge.In this paper, we propose a model-checking process with goal oriented requirements analysis, in which goal descriptions in a natural language are systematically refined into linear temporal logic formulae. Furthermore, the coverage of the verification result can be evaluated against the goal model. We developed a tool that supports the process, and applied it to an example. This process lowers the technical barriers to model checking and improves the sufficiency of system verification.

In e-business, agents need to coordinate with each other. Coordination protocols that specify the defining orders of message passing are very important. The scale of e-Business grows with the advancement of technology, and the number of agents involved continues to increase. Specifying the coordination protocols for so many participating agents is a complex task. A coordination protocol composition approach reduces the complexity of specifying such a coordination protocol. It treats coordination protocols as individual parts and composes them to construct the intended protocols. However, existing approaches do not sufficiently reduce the complexity when a coordination protocol is composed several times, because too many configurations are required to specify a composition. A new approach is proposed that uses metadata to specify the compositions by specifying only one configuration. It can reduce the number of configurations when a coordination protocol is composed several times.

Multi-agent systems need protocols to coordinate among agents implemented by different owners. However, specifying coordination protocols for many participating agents is a complex task. A protocol composition approach, which can reduce the complexity of specifying such a coordination protocol, must specify how to compose coordination protocols in a composition configuration. Current protocol compositions cannot sufficiently reduce the complexity because composing a protocol several times requires the specification of too many configurations. We propose a protocol composition approach that can specify composition configurations in an abstract way. We assign metadata to the messages in a coordination protocol and use the metadata to specify the configuration. An abstract configuration using metadata can be applied to various protocol compositions and can reduce the number of specifications required for configuration composition.

Despite the availability of a variety of program analysis tools, evaluation of these tools is difficult, as only few benchmark suites exist. Existing benchmark suites lack the uniformity needed for automation of experiments. We introduce the design of a uniform build/installation platform, which constitutes an important part of the solution. This platform is used to manage the build and test process, which is enhanced by a tool that analyzes the structure of unit tests. Benchmark applications lack detailed information about unit tests. Such knowledge is useful: For analysis algorithms that target specific program features, it is desirable to analyze only relevant tests. Using aspect-oriented programming, we wrap test execution and implement a tool providing coverage data of individual unit tests. Furthermore, the wrapper provides a front-end for the selection of subsets of a test suite. We successfully applied our tool to several large programs. This evaluation also gave us interesting insights about the quality of different test suites.

Various program analysis techniques are efficient at discovering failures and properties. However, it is often difficult to evaluate results, such as program traces. This calls for abstraction and visualization tools. We propose an approach based on UML sequence diagrams, addressing shortcomings of such diagrams for concurrency. The resulting visualization is expressive and provides all the necessary information at a glance.

Distributed hash tables (DHTs) are a class of decentralized distributed systems that can efficiently search for objects desired by the user. However, a lot of communication traffic comes from multi-word searches. A lot of work has been done to reduce this traffic by using bloom filters, which are space-efficient probabilistic data structures. There are two kinds of bloom filters: fixed-size and variable-size bloom filters. We cannot use variable- size bloom filters because doing so would mean wasting time to calculating hash values. On the other hand, when using fixed- size bloom filters, all the nodes in a DHT are unable to adjust their false positive rate parameters. Therefore, the reduction of traffic is limited because the best false positive rate differs from one node to another. Moreover, in related works, the authors took only two-word searches into consideration. In this paper, we present a method for determining the best false positive rate for three- or more word searches. We also used a new filter called a ringed filter, in which each node can set the approximately best false positive rate. Experiments showed that the ringed filter was able to greatly reduce the traffic.

Middleware for wireless sensor network (WSN) abstracts a network as an entity and hides programming difficulties from programmers. Many middlewares have been proposed, but they use different programming languages to manipulate functions in WSNs. This inhibits usability when manipulating multiple WSNs managed by different middlewares, because the primitives of each language have different descriptive capabilities. In this paper, we propose and apply design patterns in WSN programming to complement the capabilities of language primitives, and discuss the effectiveness of these design patterns. First, we discuss major middleware languages and compare the capabilities of their primitives. Second, we extract design patterns from the representative middlewares to cover the missing capabilities identified in the comparison. Finally, we discuss the effectiveness of design patterns for WSN programming. The discussion indicates that design patterns improve the usability of manipulating multiple WSNs.

This paper discusses the Top SE program established to bridge the industry-academia gap. The program features extensive use of software engineering tools, not only to introduce students to the tools, but also as a conduit for learning the techniques and guidelines needed to apply the tools to practical software development situations. The curriculum is organized around practical problems mainly from the area of digital home appliances and focuses on upper stream software development processes. The Top SE program is developed and operated by a close collaboration between industry and academia. We illustrate our discussion with examples from one of the courses, verification of design models, which takes up model checking technologies, including three specific tools: SPIN, SMV, and LTSA.

BPEL and similar languages have been provided a foundation for process-based implementation of composite Web services. These languages allow definition of orchestration processes without concrete binding information of involved partners. Although this approach facilitates customization of service discovery and selection before process execution, partner management during process execution has not been considered, such as rebinding of service providers upon events. In response to this problem, this study proposes a description model that facilitates runtime partner management in process-based services. The proposed model includes policy descriptions that allow insertion and customization of binding behavior according to surrounding environments and user preferences. The model also includes extensions in the standard process notation in order to allow process developers to give constraints to avoid adoption of inadequate policies as well as to define handlers to have common rebinding behavior reused. The proposed descriptions have been implemented and used in an agent framework for multimedia services.

This paper addresses the issue of coordination mechanism selection in agent based service oriented computing systems. We aim to strengthen the current research on service selection and composition with a dynamic choice of the underlying coordination mechanisms. Indeed, since the environment in such systems might dynamically change, and the agents can have several protocols at their disposal, they need to select the most adequate one for service provision/request upon the prevailing circumstances. In this paper, we present a joint protocol selection mechanism, which enables agents to decide together the protocols they will use to coordinate each other during a service provision.

Formal methods and requirements analysis are techniques for developing complex systems. However, there is little research on reconciling the requirements phase with the formal specification phase. To bridge this gap, we propose a formal specification generator based on model transformation techniques. This tool transforms KAOS models (requirements specifications) into VDM++ formal specifications. Our generator enables consistent and effective software development activities.

Software model checkers work directly on single-process programs, but not on multiple processes. Conversion of processes into threads, combined with a network model, allows for model checking distributed applications, but does not cover potential communication failures. This paper contributes a fault model for model checking networked programs. If a naive fault model is used, spurious deadlocks may appear, because certain processes are terminated before they can complete a necessary action. Such spurious deadlocks have to be suppressed, as implemented in our model checker extension. Our approach found several faults in existing applications, and scales well because exceptions generated by our tool can be checked individually.

The purpose of this study is to reduce the gap between the requirement analysis and analysis phases of developing multi-agent systems. We utilize KAOS, one of the goal-oriented analysis methodologies, as a requirement analysis method, and propose a model translation into an analysis model for simple and effective development of multi-agent systems.

Negotiation between robots is often needed in wireless ad-hoc robot network. In dynamic coverage problem, wireless ad-hoc robots move in the field negotiating with other robots frequently. Robots are tied with P2P network and determine their actions autonomously by negotiations with other robots. The topic of this paper is a negotiation for multiagent system (MAS) in the problem of robots activity in dynamic coverage, which is the problem of robots to cover all the areas of a free space by their sensors in the shortest possible time. This paper introduce a novel algorithm for robots' negotiation for MAS in the dynamic coverage problem by using PID-filter. This algorithm reduced useless motion of robots and decreased the time concerning the convergence of a robot's action for dynamic coverage problem. The claim made in this paper for new negotiation algorithm for MAS are supported by comprehensive experimental results and discusses future research directions

A location-specific data retrieval, which is data retrieval from nodes in a designated region at the time, is an attractive application in a mobile ad-hoc network (MANET). However, almost all nodes in a MANET are powered by batteries, the location-specific data retrieval should involve a small number of messages. In this paper, we use a mobile agent to retrieve the location-specific data. A mobile agent migrates to a node in a designated region, and retrieves data from nodes in this region. Since, after migration, the agent can communicate with nodes in the designated region through low overhead short length hops, the mobile agent can retrieve data at low message cost for long periods, even if the owner of this agent moves around. However, even after migrating to node in the designated region, in order to stay near this region, a mobile agent should migrate to other nodes in response to the movement of the node hosting this agent. In this paper, we propose the geographically bound mobile agent (GBMA) which is a mobile agent that periodically migrates in order to always be located in a designated region. In order to clarify where the GBMA should be located and when the GBMA starts to migrate, two geographic zones are set to the GBMA: required zone and expected zone. The required zone ease tracking of the GBMA, and the expected zone ease adjustment of the GBMA migration timing.

Web services technology for interoperability, dynamic discovery and integration of distributed components is to be applied in various environments, such as networks of relatively narrow and unstable wireless connections. However, in such environments it is necessary to deal with resource limitation. Our work adopts mobile agent technology in response to this problem and presents a mobile agent framework for Web Services integration. This framework utilizes BPEL (Business Process Execution Language for Web Services) for description of the integration logic, and provides declarative descriptions for physical behaviors (migration and cloning). This facilitates addition or change of physical behaviors according to the environmental conditions while preserving the integration logic. This paper especially focuses on formal definition and discussion of our framework.

Enterprise information systems are currently developed as Web-based applications in the service-oriented architecture style, which is implied by the stack of Web services standards. One of the most critical aspects in enterprise information systems is to maintain their continuous operation as long as possible. Conventional service invocation mechanism identifies Web services with similar signatures found by a service discovery as distinctively different ones, resulting in the suspension of a service requester and the modification of its invocation procedures. In this paper, we propose a reasonable solution to realize runtime invocation of Web services, regarding structural similarities in service signatures as subsumption relations of XML scheme. Given Web services with subsumed signatures, this solution enables a service requester to invoke the Web services by translating the scheme types of the invocation parameters.

As mobile computing becomes common, the battery issue of mobile computing devices has become increasingly notable. To this end, research and development of various power-conservation devices and methods are actively taking place. However, the conventional method of extending the battery life through power-conservation can never prevent the unintentional shutdowns of applications due to a dead battery. This research aims to realize the evacuation of applications on a mobile computing device to another device before the battery runs out by creating the application as a mobile agent. In particular, by introducing the concept of the Crisis Management Center, dynamic and smooth evacuation of multiple application agents will become possible. The paper explains and verifies the effectiveness of the EASTER (Escape Agent System from dying batTERy), a system developed for the purpose of recovering the applications when a battery is running out through the use of a mobile agent system.

As wide-area open networks like the Internet and intranets grow larger, mobile agent technology is attracting more attention. Agents are units of software that can deal with environmental changes and the various requirements of open networks through features such as autonomy, mobility, intelligence, cooperation, and reactivity. However, since the usual development methods of the agent systems are not sufficiently investigated, the technology is not yet widespread. In previous papers, we introduced behavior patterns for mobile agent systems and the development method. The behavior patterns represent typical and recurring structures and behaviors of agents. The patterns are classified according to their appropriate architectural levels and the degree to which they depend on specific agent platforms. We evaluate the advantages of our method from the development process viewpoints. Our evaluation focuses on the development efficiency, the applicability, the extensibility and the understandability of our method.

We propose a new concept called Rental Service. Recently, Rental Application has been applied as ASP (Application Service Provider). For the next step in advancing ASP, we would prefer to provide Service which creates new functions, coordinating and facilitating already-existing functions, provided by many applications in the network. In this paper, we firstly made a list of problems for adding such service into the current ASP systems. Then, we developed a framework as a solution to the problems, in which the service itself is regarded as an agent. Moreover, we introduced a practical system developed by the framework, and finally examined its effectiveness for the above problems through that practical case.

Interoperability between different systems is becoming a more important issue as computer networks expand. In this paper, we propose Bee-gent (Bonding and Encapsulation Enhancement Agent), a distributed system development-framework that aims to provide coordination between RDBMSs, legacy systems, software packages, and so forth. The Bee-gent coordination mechanism is an integration of interaction protocols that explicitly describe the relationship of the elements of a distributed system. Our approach is to split interaction protocols into the flow for the overall problem solving sequence and the location dependent local flow. Further mobile mediation agents coordinate the behavior of each element according to the flow of the problem solving sequence. We present an example of system development using Bee-gent that demonstrates how maintenance becomes simpler in the face of modifying the distributed system components.

As wide-area open networks such as the Internet and intranets grow larger, agent technology is attracting more attention.Agents are units of software that can deal with environmental changes and the various requirements of open networks through features such as autonomy, mobility, intelligence, cooperation, and reactivity.However, since the usual development method of the agent systems is not sufficiently investigated, the technology is not yet widespread.This paper proposes a method of agent system development based on agent patterns that represent typical and recurring structures and behaviors of agents.The agent patterns are classified according to their appropriate architectural levels and the degree to which they depend on specific agent platforms.Our method enables developers to design agent systems efficiently since' they can construct complicated system architectures and behaviors by dividing the design process into two architectural levels and applying the appropriate agent patterns.In addition, the higher level designs are independent of specific agent platforms and can be therefore easily reused.

This paper proposes a method of agent system development based on agent patterns that represent typical and recurring structures and behaviors of agents. The agent patterns are classified according to their appropriate architectural levels and the degree of their dependence on specific agent platforms. Our method enables developers to design agent systems efficiently since they can construct complicated system architectures and behaviors by dividing the design process into two architectural levels and applying the appropriate agent patterns. In addition, the higher level designs are independent of specific agent platforms and can be therefore easily reused.

The authors' goal is to establish a flexible programming support for evolving systems that will enable one to modify programs using less labor, while maintaining good quality during service life. EVA (evolution mechanism for flexible agent) was developed to allow a flexible programming support system to be constructed based on their programming method for evolving systems. They consider that programming methods for evolving systems need to satisfy the following essential conditions. First, they need to make it easy to specify changes in a system in terms of new requirements. Second, they need to have a procedure for transmitting the new requirements to a program. Third, they need to be able to guarantee that the resultant program will meet the new requirements. Finally, because of the repetitive nature of much evolving systems work, they need to provide for the reuse of similar modifications during programming. In order to overcome limitations in program modification techniques which have only considered programming products, programming processes have been introduced into the method. To achieve their goal, they have formulated programming products and programming processes using such formal techniques as functional programming, type theory, modules, parameterized programming and natural deduction, and have constructed a mechanism of reusing these formal programming processes. The paper explains a case study which shows how to develop an evolving system using EVA and it discusses how one can use EVA's mechanism effectively.

The paper presents a technique for reusing design processes which aims at facilitating software reuse. The authors have constructed a specification reuse model based on two major ideas. One is to extract domain specific specification parts, and the other is to reuse design processes represented by reuse histories. In addition, a domain analysis method is proposed to construct the specification reuse model. This model has been evaluated through experimental applications. The results show that the model facilitates software reuse, and that the domain analysis clarifies the application scope of the model. Applicability of our model to other domains is also discussed.

We propose two new concepts, evolutional agent and field. The main purpose of the work is to provide a framework for building software which adapts to changes of requirements autonomously. In open networks, the adaptability to changes of requirements or environments is essential, while there are many free applications and components to serve various requirements. Focusing on this point, we introduce concurrent objects with meta-base architecture, agents, and fields, another kind of object. In our model, adaptive agents, called evolutional agents, adapt to changes by traversing a network and acquiring components as their own functions. Fields are receptacles of software components in networks. Agents evolve into enhanced ones by traveling to fields and acquiring components from the fields. Flage is a framework for building the software architecture based on these concepts.

Our research goal is to construct an environment that mechanically supports human intellectual activities through software development with reuse. We propose a model of software development with systematic reuse in which the existing program can be altered to meet the given specification change using the program derivation process from the specification. We consider what kinds of knowledge must be acquired when a specification is changed and how they have to be integrated so well as to implement our method on a computer system. In particular, we focus on the following points in terms of program synthesis. First, a wide spectrum language, Extended IML, defines a framework in which specifications, program and the elaborate derivation process can be expressed formally on a computer system. This forms a basic unit of reusable knowledge for specification change. Second, a reuse process is formulated in which a new requirement changes the efficient specification and its program is altered to meet this requirement correctly. Third, we consider some conditions and a mechanism by which the knowledge can be integrated so as to obtain an objective program automatically, implying that we will yet profit from reusing the knowledge.

Although many CASE tools for object-oriented methods (OO CASE tools) have been proposed, few, if any, can verify that the constructed analysis and design models actually match the requirements of the system being developed. In order to realize this kind of verification, we propose a software development method amalgamating OO CASE tools and algebraic specification techniques. We are developing an experimental system named COOAD (CASE tool for Object-Oriented Analysis and Design) in order to examine the effectiveness of our proposition. COOAD supports object-oriented analysis and design, verification of the analysis and design, and generation of code. In this paper, we propose the software development method, introduce COOAD, and illustrate the facilities of COOAD with an example.

Although many CASE tools for object-oriented methods (OO-CASE tools) have been proposed, few, if any, can verify that the constructed analysis and design models actually match the requirements of the system being developed. In order to realize this kind of verification, we propose a software development method amalgamating OO-CASE tools and algebraic specification techniques. We are developing an experimental system named COOAD (CASE tool for Object-Oriented Analysis and Design) in order to examine the effectiveness of our proposition. COOAD supports object-oriented analysis and design, verification of the analysis and design, and generation of code. In this paper, we propose the software development method, introduce COOAD, and illustrate the facilities of COOAD with several examples.>

We propose Flage, a language for building software modules (agents) which adapt to a change of environment in an open distributed system. Environments are defined as multiple agents and specifications which consist of constraints for cooperation among the agents. Flage provides a description of environments by the notion of agents and fields. We describe the concept of Flage and the adaptation technique.>

The paper describes an application of artificial intelligence technology to the implementation of a rapid prototyping method in object-oriented performance design (OOPD) for real-time systems. OOPD consists of two prototyping phases for real-time systems. Each of these phases consists of three steps: prototype construction, prototype execution, and prototype evaluation. We present artificial intelligence based methods and tools to be applied to the individual steps. In the prototype construction step, a rapid construction mechanism using reusable software components is implemented based on planning. In the prototype execution step, a hybrid inference mechanism is used to execute the constructed prototype described in declarative knowledge representation. MENDEL, which is a Prolog based concurrent object-oriented language, can be used as a prototype construction tool and a prototype execution tool. In the prototype evaluation step, an expert system which is based on qualitative reasoning is implemented to detect and diagnose bottlenecks and generate an improvement plan for them.>

A software prototyping environment called TransObj (TRANSaction and OBJect) is used for designing real-time Transaction-based Concurrent Software Systems (TCSS). In a TCSS design process, a software designer should perform both functional design and performance design. The designer should change his design view from a transaction-based paradigm to an object-based paradigm during the TCSS design process. Recognition of re-entrant functional objects and serially reusable functional objects in the TCSS should be required. TransObj includes the Stepwise Prototyping Method (SPM), and two SPM-based tools: Prolog-based TransObj (P-TransObj) and GPSS-based TransObj (G-TransObj). SPM enables the designer to advance both functional design and performance design for the TCSS prototype as controling the change of design view paradigms. P-TransObj mainly checks the prototype in a microscopic view on a personal computer. G-TransObj mainly checks the same prototype with a longer time span on a large-scale computer.

The qualitative method is proposed for parameter tuning of synchronized queuing network bottlenecks (SQN). The bottleneck diagnosis expert system for SQN (BDES-S) can carry out parameter tuning in order to diagnose and improve bottlenecks of SQN. BDES-S can produce several alternative qualitative improvement plans for the bottleneck. BDES-S is a novel system for applying the qualitative reasoning to the SQN. >