Quality of service (QoS) time series forecasting for web services (WSs) has been studied for over a decade. In recent years, this problem has been investigated in its multistep-ahead version (namely, the problem where the prediction horizon is greater than one) for the long-term rental and use of cloud-based WSs. To solve this multistep-ahead QoS time series forecasting problem, previous research has adopted single-predictor-based strategies and conventional time series methods, such as autoregressive integrated moving average (ARIMA) models and exponential smoothing. In this article, however, we propose applying genetic programming (GP) to search for and evolve a set of multiple predictors, in which each predictor is dedicated to forecasting a specific future time point. Our GP-based approach proposes and tests two types of multiple predictors that differ from the consumed predictor inputs that drive each predictor to produce its QoS forecasting results. In the first type, the input of each predictor is a sequence of fixed previous QoS observations; in the second type, each predictor dynamically consumes the most recent QoS values, which could consist of the forecasting results of its previous predictors). Furthermore, we propose two techniques for our multipredictor-based GP approach, namely, elite individual composition (EIC) and hybrid evolution, and apply them to enhance the forecasting accuracy of our approach. Finally, based on a real-world QoS time series dataset, the proposed approach is validated and compared with several conventional methods to demonstrate its superiority in terms of accuracy; in addition, the effectiveness and efficiency of the proposed multiple predictors and the two techniques are verified in the experiment.

Web API/service technology has been attracting considerable attention and the REST (REpresentational State Transfer) architecture is now widely accepted as mainstream technology. Nonetheless, developing the means by which to discover RESTful Web APIs/services is crucial to the further development of this technology. Unfortunately, existing search engines for RESTful Web APIs/services provide only keyword-based or tag-based search functions. A failure to take into account the semantics and/or characteristics (e.g., their interface compatibility) greatly hampers the ability to find suitable APIs/services. In this study, we propose a novel approach to the discovery of RESTful Web API/services, referred to as Test-Oriented API Search with Semantic Interface Compatibility (TASSIC). This scheme involves expanding the terms of Web API/service documents based on DBpedia and WordNet. Unsuitable APIs/services are then filtered out using a systematic process, as follows: 1) calculation of semantic similarity between a set of candidate APIs/services and a user query, 2) analysis of interface compatibility between candidate APIs/services using the Hungarian Algorithm, 3) invocation of candidate APIs/services to verify functionality and availability, and 4) analysis of similarity between the actual response of the candidate services and the expected response specified in the user query. The proposed TASSIC increases the likelihood of matching APIs/services that are semantically equivalent or similar to user queries. Unit test and acceptance test are used to verify that a set of candidate APIs/services are actually available and that they actually meets user requirements. Experiment results demonstrate the efficacy of TASSIC, the accuracy of which is superior to that of existing methods.

Currently, statistical time-series methods have primarily been employed to predict time-aware dynamic quality of service (QoS) attributes for Web services. In this paper, we propose the application of genetic programming (GP) for such predictions. Our experimental results indicate that the GP-based approach is more accurate than the other approaches presented for comparison. However, for the efficient management of such attributes for cloud services, including their modeling and forecasting, the current research is insufficient because a set of research questions remains unanswered. In this paper, we first clearly define these research questions and then design and perform a set of empirical experiments to address the questions. Finally, the experimental results are exhaustively discussed to answer the studied research questions. The empirical study and analysis presented in this paper could be informative for the management (modeling and forecasting) of the time-aware dynamic QoS attributes of cloud services. For example, we verify that machine-learning approaches are generally superior to the widely used statistical time-series methods in terms of both modeling accuracy and forecasting accuracy. Furthermore, after considering a variety of situations and cases, the GP-based approach is still the best option for the studied problem. In addition, except for the technical approaches, this paper also exhaustively studies the influence of the properties of the cloud dynamic QoS attributes, including their size and time granularity.

In recent years, automated service composition has been a fervid research area in service computing. Within this area, service description plays a crucial role in terms of the development of a diverse number of automation schemes. In this paper, we provide an investigation and classification of the service description approaches that have been used in a diverse collection of automated service composition studies. To position the service description approaches used in automated service composition throughout the service description world and to clearly classify them, we propose five two-value dimensions. Using the proposed dimensions, we first perform a categorization and provide a simple introduction to current representative industrial service description standards. Subsequently, because we discovered that most of the studied automated composition approaches follow a tuple-based service description paradigm, an exhaustive classification and discussion of this paradigm is made, with the automated composition approaches adopting this paradigm as an example. Finally, we discuss issues that are currently relevant to the service description field and possible solutions. With this study, the reader can obtain a complete understanding of service description approaches used in automated service composition research, including their common formulation and assumptions.

This paper proposes an argumentation and negotiation mechanism for multi-agent systems. Through argumentations and negotiations, agents obtain more information on the topics of common interests or on those they have odds with. At the inception of the negotiation, agents can hardly understand completely the goals and beliefs other agents have toward related issues. Through argumentations and negotiations, the beliefs evolve, and agents will have better understanding about each other's target needs and preferences. During negotiations, agents can select the proposal that better suits other agents, further improving the chances for the agents to reach a consensus. Lastly, this paper illustrates our proposed methods through a simple course-scheduling negotiating system.

Service-oriented architecture implemented by Web Services is one of the most popular and promising software development paradigm that has brought some challenging research issues today. One of the most important issues is how to automate web service composition at design phase. Currently, there are many researchers concentrating on service composition problem that can be partitioned into three parts, dynamic workflow composition, QoS-aware, and transaction-aware service selection. This paper addresses the issue of automatic composing Web Services into an executable workflow not only according to user's functional requirements but also to their transactional properties and QoS characteristics. We propose an automatic composition approach through genetic algorithm to satisfy user's functional requirements, QoS criteria, and transactional requirements automatically at the same time. Experimental results are presented.

A number of approaches have been proposed in tackling the timetabling problem, such as operational research, human-machine interaction, constraint programming, expert systems, and neural networks. However, there are still several key challenges to be addressed: easily reformulated to support changes, a generalized framework to handle various timetabling problems, and ability to incorporate knowledge in the timetabling system. In this article, we propose an automatic software engineering approach, called task-based conceptual graphs, to addressing the challenges in the timetabling problem. Task-based conceptual graphs provide the automation of software development processes including specification, verification, and automatic programming. Maintenance can be directly performed on the specifications rather than on the source code; moreover, hard and soft constraints can be easily inserted or removed. A university timetabling system in the Department of Computer Science and Information Engineering at National Central University is used as an illustrative example for the proposed approach. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1137–1160, 2005.

Fuzzy theory is suitable to capture and analyze the informal requirements that are imprecise in nature, meanwhile, XML is emerging as one of the dominant data formats for data processing on the Internet. In this paper, we attempt to markup the fuzzy objects with XML, and provide conversion rules to define the mapping from fuzzy objects model and fuzzy objects specification into XML schema and XML documents, respectively.

In this paper, we have attempted a study of current approaches carried out in the confluence of the two technologies: fuzzy set theory and software engineering, that could provide a powerful tool for requirements engineering, formal specifications, software quality prediction, object-oriented modeling, and etc. Various requirements analysis and specifications modeling technologies that utilize fuzzy theory are identified, and works related to the use of fuzzy logic for predicting software quality are also outlined.

The focus of the paper is on the use of time Petri nets to serve as the verification mechanism for acquired scenarios. Use cases are used to elicit the user needs and to derive the scenarios. After specifying all possible scenarios, each of them can be transformed into its corresponding time Petri nets model (TPN). Through the analysis of these TPN models, wrong information and missing information in scenarios can be detected. The proposed approach is illustrated by a course registration problem domain.

It is widely recognized that requirements engineering is a knowledge-intensive process and cannot be dealt with using only a few general principles. Since knowledge plays a crucial role in software development, software engineers have to describe and organize various aspects of knowledge before the program can be written. A recent work by Robillard reveals that software development can be further improved by recognizing the relevant knowledge structures. In this paper, we propose the use of a task-based conceptual graphs (TBCB) framework as a basis for automating software development. By structuring and operationalizing conceptual graphs, TBCG specifications can be transformed into executable programs automatically. To construct a conceptual model, task-based specification methodology is used as the mechanism to structure the knowledge captured in conceptual models, whereas conceptual graphs are adopted as the formalism to express task-based specifications and to provide a reasoning capability for the purpose of automation. Once task-based graphs have been constructed and verified, a blackboard system will automatically transform TBCG specifications into a software system composed of database schemas, knowledge base, and user interfaces. A meeting scheduling system is used as an illustrative example to demonstrate our approach. © 2000 John Wiley & Sons, Inc.