A fuzzy regression and optimization approach for setting target levels in software quality function deployment

With the rapid development of the software industry, improving the quality of software development has gained increasing importance. Software manufacturers have recently applied quality improvement techniques to software development to respond to the needs for software quality. Software quality function deployment (SQFD), as a technique for improving the quality of the software development process to create products responsive to customer expectations, is used to maximize customer satisfaction. This paper presents a fuzzy regression and optimization approach to determine target levels in SQFD. The inherent fuzziness of relationships in SQFD modeling justifies the use of fuzzy regression. Fuzzy regression is used to identify the functional relationships between customer requirements and technical attributes, and among technical attributes. Then, a mathematical programming model is developed to determine target levels of technical attributes using the functional relationships obtained by fuzzy regression. A search engine quality improvement problem is presented to illustrate the application of the proposed approach.     

Agent-based Drivers’ Information Assistance System

“Drivers’ Information Assistance System (DIA system)” is an ITS (Intelligent Transport Systems) application framework that provides agent-based information assistance to drivers through car navigation systems or on-board PCs. DIA system enables flexible information retrieval over the Internet using intelligent mobile agent, and incorporates a high-speed event delivery facility that makes real-time information service possible. The goal of the system is to provide up to the minute information and services related to driver needs, such as parking lot vacancy information. Crucial to making this a practical operation is the agent-based ability to access the network while the vehicle is in motion. Masanori Hattori: He is a research engineer in the Computer & Network Systems Laboratory, Corporate Research & Development Center, Toshiba Corporation. His research interests are network computing, human interface, and agent technologies especially in mobile agents, intelligent agents, and physical agents. He received the B.E. and M.E. from the Kyushu University. Naoki Kase: He received the M.S. in computer science from the Keio University, Japan. His research interests are mobile agent and its applications. He has developed an intelligent mobile agent system and its applications on ITS (Intelligent Transport Systems) field. Akihiko Ohsuga, Dr. Eng.: He is a senior research scientist at the Computer & Network Systems Laboratory in Toshiba Corporation. Dr. Ohsuga received a B.S. degree in mathematics from Sophia University in 1981 and a Dr. Eng. degree in electrical engineering from Waseda University in 1995. He joined Toshiba Corporation in 1981, worked with the ICOT (institute for New Generation Computer Technology) involved in the Fifth Generation Computer System project from 1985 to 1989. His research interests include agent technologies, formal specification & verification, and automated theorem proving. Shinichi Honiden, Dr.Eng.: He is a chief specialist of Government Division, Toshiba Corporation. He received the B.S., M.S., and Dr. Eng. degrees in electrical engineering from Waseda University, Tokyo, Japan, in 1976, 1978, and 1986, respectively. Since 1978, he has been with Toshiba Corporation. His research interests include software engineering and artificial intelligence. In these fields, he is the author or coauthor of ten textbooks and has published over 80 technical papers.     

An empirical study of predicting software faults with case-based reasoning

The resources allocated for software quality assurance and improvement have not increased with the ever-increasing need for better software quality. A targeted software quality inspection can detect faulty modules and reduce the number of faults occurring during operations. We present a software fault prediction modeling approach with case-based reasoning (CBR), a part of the computational intelligence field focusing on automated reasoning processes. A CBR system functions as a software fault prediction model by quantifying, for a module under development, the expected number of faults based on similar modules that were previously developed. Such a system is composed of a similarity function, the number of nearest neighbor cases used for fault prediction, and a solution algorithm. The selection of a particular similarity function and solution algorithm may affect the performance accuracy of a CBR-based software fault prediction system. This paper presents an empirical study investigating the effects of using three different similarity functions and two different solution algorithms on the prediction accuracy of our CBR system. The influence of varying the number of nearest neighbor cases on the performance accuracy is also explored. Moreover, the benefits of using metric-selection procedures for our CBR system is also evaluated. Case studies of a large legacy telecommunications system are used for our analysis. It is observed that the CBR system using the Mahalanobis distance similarity function and the inverse distance weighted solution algorithm yielded the best fault prediction. In addition, the CBR models have better performance than models based on multiple linear regression. Taghi M. Khoshgoftaar is a professor of the Department of Computer Science and Engineering, Florida Atlantic University and the Director of the Empirical Software Engineering Laboratory. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence, computer performance evaluation, data mining, and statistical modeling. He has published more than 200 refereed papers in these areas. He has been a principal investigator and project leader in a number of projects with industry, government, and other research-sponsoring agencies. He is a member of the Association for Computing Machinery, the IEEE Computer Society, and IEEE Reliability Society. He served as the general chair of the 1999 International Symposium on Software Reliability Engineering (ISSRE’99), and the general chair of the 2001 International Conference on Engineering of Computer Based Systems. Also, he has served on technical program committees of various international conferences, symposia, and workshops. He has served as North American editor of the Software Quality Journal, and is on the editorial boards of the journals Empirical Software Engineering, Software Quality, and Fuzzy Systems. Naeem Seliya received the M.S. degree in Computer Science from Florida Atlantic University, Boca Raton, FL, USA, in 2001. He is currently a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. His research interests include software engineering, computational intelligence, data mining, software measurement, software reliability and quality engineering, software architecture, computer data security, and network intrusion detection. He is a student member of the IEEE Computer Society and the Association for Computing Machinery.     

Dual-coding representations for robot vision programming in Tekkotsu

We describe complementary iconic and symbolic representations for parsing the visual world. The iconic pixmap representation is operated on by an extensible set of “visual routines” (Ullman, 1984; Forbus et al., 2001). A symbolic representation, in terms of lines, ellipses, blobs, etc., is extracted from the iconic encoding, manipulated algebraically, and re-rendered iconically. The two representations are therefore duals, and iconic operations can be freely intermixed with symbolic ones. The dual-coding approach offers robot programmers a versatile collection of primitives from which to construct application-specific vision software. We describe some sample applications implemented on the Sony AIBO. David S. Touretzky is a Research Professor in the Computer Science Department and the Center for the Neural Basis of Cognition at Carnegie Mellon University. He earned his B.A. in Computer Science from Rutgers University in 1978, and his M.S. (1979) and Ph.D. (1984) in Computer Science from Carnegie Mellon. Dr. Touretzky’s research interests are in computational neuroscience, particularly representations of space in the rodent hippocampus and related structures, and high level primitives for robot programming. He is presently developing an undergraduate curriculum in cognitive robotics based on the Tekkotsu software framework described in this article. Neil S. Halelamien earned a B.S. in Computer Science and a B.S. in Cognitive Science at Carnegie Mellon University in 2004, and is currently pursuing his Ph.D. in the Computation & Neural Systems program at the California Institute of Technology. His research interests are in studying vision from both a computational and biological perspective. He is currently using transcranial magnetic stimulation to study visual representations and information processing in visual cortex. Ethan J. Tira-Thompson is a graduate student in the Robotics Institute at Carnegie Mellon University. He earned a B.S. in Computer Science and a B.S. in Human-Computer Interaction in 2002, and an M.S. in Robotics in 2004, at Carnegie Mellon. He is interested in a wide variety of computer science topics, including machine learning, computer vision, software architecture, and interface design. Ethan’s research has revolved around the creation of the Tekkotsu framework to enable the rapid development of robotics software and its use in education. He intends to specialize in mobile manipulation and motion planning for the completion of his degree. Jordan J. Wales is completing a Master of Studies in Theology at the University of Notre Dame. He earned a B.S. in Engineering (Swarthmore College, 2001), an M.Sc. in Cognitive Science (Edinburgh, UK, 2002), and a Postgraduate Diploma in Theology (Oxford, UK, 2003). After a year as a graduate research assistant in Computer Science at Carnegie Mellon, he entered the master’s program in Theology at Notre Dame and is now applying to doctoral programs. His research focus in early and medieval Christianity is accompanied by an interest in medieval and modern philosophies of mind and their connections with modern cognitive science. Kei Usui is a masters student in the Robotics Institute at Carnegie Mellon University. He earned his B.S. in Physics from Carnegie Mellon University in 2005. His research interests are reinforcement learning, legged locomotion, and cognitive science. He is presently working on algorithms for humanoid robots to maintain balance against unexpected external forces.     

The pairwise attribute noise detection algorithm

Analyzing the quality of data prior to constructing data mining models is emerging as an important issue. Algorithms for identifying noise in a given data set can provide a good measure of data quality. Considerable attention has been devoted to detecting class noise or labeling errors. In contrast, limited research work has been devoted to detecting instances with attribute noise, in part due to the difficulty of the problem. We present a novel approach for detecting instances with attribute noise and demonstrate its usefulness with case studies using two different real-world software measurement data sets. Our approach, called Pairwise Attribute Noise Detection Algorithm (PANDA), is compared with a nearest neighbor, distance-based outlier detection technique (denoted DM) investigated in related literature. Since what constitutes noise is domain specific, our case studies uses a software engineering expert to inspect the instances identified by the two approaches to determine whether they actually contain noise. It is shown that PANDA provides better noise detection performance than the DM algorithm. Jason Van Hulse is a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. His research interests include data mining and knowledge discovery, machine learning, computational intelligence and statistics. He is a student member of the IEEE and IEEE Computer Society. He received the M.A. degree in mathematics from Stony Brook University in 2000, and is currently Director, Decision Science at First Data Corporation. Taghi M. Khoshgoftaar is a professor at the Department of Computer Science and Engineering, Florida Atlantic University, and the director of the Empirical Software Engineering and Data Mining and Machine Learning Laboratories. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence, computer performance evaluation, data mining, machine learning, and statistical modeling. He has published more than 300 refereed papers in these subjects. He has been a principal investigator and project leader in a number of projects with industry, government, and other research-sponsoring agencies. He is a member of the IEEE, the IEEE Computer Society, and IEEE Reliability Society. He served as the program chair and general chair of the IEEE International Conference on Tools with Artificial Intelligence in 2004 and 2005, respectively. Also, he has served on technical program committees of various international conferences, symposia, and workshops. He has served as North American editor of the Software Quality Journal, and is on the editorial boards of the journals Empirical Software Engineering, Software Quality, and Fuzzy Systems. Haiying Huang received the M.S. degree in computer engineeringfrom Florida Atlantic University, Boca Raton, Florida, USA, in 2002. She is currently a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. Her research interests include software engineering, computational intelligence, data mining, software measurement, software reliability, and quality engineering.     

Resource oriented selection of rule-based classification models: An empirical case study

The amount of resources allocated for software quality improvements is often not enough to achieve the desired software quality. Software quality classification models that yield a risk-based quality estimation of program modules, such as fault-prone (fp) and not fault-prone (nfp), are useful as software quality assurance techniques. Their usefulness is largely dependent on whether enough resources are available for inspecting the fp modules. Since a given development project has its own budget and time limitations, a resource-based software quality improvement seems more appropriate for achieving its quality goals. A classification model should provide quality improvement guidance so as to maximize resource-utilization. We present a procedure for building software quality classification models from the limited resources perspective. The essence of the procedure is the use of our recently proposed Modified Expected Cost of Misclassification (MECM) measure for developing resource-oriented software quality classification models. The measure penalizes a model, in terms of costs of misclassifications, if the model predicts more number of fp modules than the number that can be inspected with the allotted resources. Our analysis is presented in the context of our Rule-Based Classification Modeling (RBCM) technique. An empirical case study of a large-scale software system demonstrates the promising results of using the MECM measure to select an appropriate resource-based rule-based classification model. Taghi M. Khoshgoftaar is a professor of the Department of Computer Science and Engineering, Florida Atlantic University and the Director of the graduate programs and research. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence applications, computer security, computer performance evaluation, data mining, machine learning, statistical modeling, and intelligent data analysis. He has published more than 300 refereed papers in these areas. He is a member of the IEEE, IEEE Computer Society, and IEEE Reliability Society. He was the general chair of the IEEE International Conference on Tools with Artificial Intelligence 2005. Naeem Seliya is an Assistant Professor of Computer and Information Science at the University of Michigan - Dearborn. He recieved his Ph.D. in Computer Engineering from Florida Atlantic University, Boca Raton, FL, USA in 2005. His research interests include software engineering, data mining and machine learnring, application and data security, bioinformatics and computational intelligence. He is a member of IEEE and ACM.     

Integrating On-Line Performance Visualization and Real-Time System Design

Emerging parallel or distributed, heterogeneous real-time computer systems with many disparate constraints and requirements would benefit from a unifying and comprehensive systems engineering support in the design, testing and deployment phases, which interfaces with a human at a very high level and efficiently handles the system complexity. We present an approach to integration of (1) a compiler- and Constraint Logic Programming-based approach to design and engineering of real-time systems, and (2) an on-line performance analysis and visualization technology. An example distributed real-time system is used to help describe the integrated approach. Details are presented about how the involved technologies are used to help system developers and users achieve better system performance through on-line repair and reconfiguration. This work was supported in part by NSF grants No. 0334035, 0098017, and EIA-9911074 and NIH grant no.EB002238-01. Aleksandar M. Bakić is a lead engineer at Vlatacom, Ltd. His research interests include design and engineering of complex, distributed real-time systems; instrumentation, performance visualization and steering; high-level programming languages, compiler-based technologies and software design automation. He received his BS in computer engineering from the School of Electrical Engineering, Belgrade, Serbia and Montenegro, and his MS and PhD in computer science from Michigan State University. Matt Mutka received the B.S. degree in electrical engineering from the University of Missouri-Rolla in 1979, the M.S. degree in electrical engineering from Stanford University in 1980, and the Ph.D. degree in Computer Science from the University of Wisconsin-Madison in 1988. In 1989 he joined the faculty of the Department of Computer Science, Michigan State University, East Lansing, Michigan, where he is currently an associate professor.† He was a visiting scholar at the University of Helsinki, Helsinki Finland, in 1988–1989, and in 2002, and a member of technical staff at Bell Laboratories in Denver, Colorado from 1979–1982. His current research interests include mobile computing, wireless networking, multimedia networking, and network security issues.     

Visual wheel sinkage measurement for planetary rover mobility characterization

Wheel sinkage is an important indicator of mobile robot mobility in natural outdoor terrains. This paper presents a vision-based method to measure the sinkage of a rigid robot wheel in rigid or deformable terrain. The method is based on detecting the difference in intensity between the wheel rim and the terrain. The method uses a single grayscale camera and is computationally efficient, making it suitable for systems with limited computational resources such as planetary rovers. Experimental results under various terrain and lighting conditions demonstrate the effectiveness and robustness of the algorithm. Christopher Brooks is a graduate student in the Mechanical Engineering department of the Massachusetts Institute of Technology. He received his B.S. degree with honor in engineering and applied science from the California Institute of Technology in 2000, and his M.S. degree from the Massachusetts Institute of Technology in 2004. He is a student collaborator on the Mars Exploration Rover science mission. His research interests include mobile robot control, terrain sensing, and their application to improving autonomous robot mobility. He is a member of Tau Beta Pi. Karl Iagnemma is a research scientist in the Mechanical Engineering department of the Massachusetts Institute of Technology. He received his B.S. degree summa cum laude in mechanical engineering from the University of Michigan in 1994, and his M.S. and Ph.D. from the Massachusetts Institute of Technology, where he was a National Science Foundation graduate fellow, in 1997 and 2001, respectively. He has been a visiting researcher at the Jet Propulsion Laboratory. His research interests include rough-terrain mobile robot control and motion planning, robot-terrain interaction, and robotic mobility analysis. He is author of the monograph Mobile Robots in Rough Terrain: Estimation, Motion Planning, and Control with Application to Planetary Rovers (Springer, 2004). He is a member of IEEE and Sigma Xi. Steven Dubowsky received his Bachelor's degree from Rensselaer Polytechnic Institute of Troy, New York in 1963, and his M.S. and Sc.D. degrees from Columbia University in 1964 and 1971. He is currently a Professor of Mechanical Engineering at M.I.T and Director of the Mechanical Engineering Field and Space Robotics Laboratory. He has been a Professor of Engineering and Applied Science at the University of California, Los Angeles, a Visiting Professor at Cambridge University, Cambridge, England, and Visiting Professor at the California Institute of Technology. During the period from 1963 to 1971, he was employed by the Perkin-Elmer Corporation, the General Dynamics Corporation, and the American Electric Power Service Corporation. Dr. Dubowsky's research has included the development of modeling techniques for manipulator flexibility and the development of optimal and self-learning adaptive control procedures for rigid and flexible robotic manipulators. He has authored or co-authored nearly 300 papers in the area of the dynamics, control and design of high performance mechanical and electromechanical systems. Professor Dubowsky is a registered Professional Engineer in the State of California and has served as an advisor to the National Science Foundation, the National Academy of Science/Engineering, the Department of Energy, and the US Army. He is a fellow of the ASME and IEEE and is a member of Sigma Xi and Tau Beta Pi.     

A New Challenge for Applying Time Series Metrics Data to Software Quality Estimation

In typical software development, a software reliability growth model (SRGM) is applied in each testing activity to determine the time to finish the testing. However, there are some cases in which the SRGM does not work correctly. That is, the SRGM sometimes mistakes quality for poor quality products. In order to tackle this problem, we focussed on the trend of time series data of software defects among successive testing phases and tried to estimate software quality using the trend. First, we investigate the characteristics of the time series data on the detected faults by observing the change of the number of detected faults. Using the rank correlation coefficient, the data are classified into four kinds of trends. Next, with the intention of estimating software quality, we investigate the relationship between the trends of the time series data and software quality. Here, software quality is defined by the number of faults detected during six months after shipment. Finally, we find a relationship between the trends and metrics data collected in the software design phase. Using logistic regression, we statistically show that two review metrics in the design and coding phase can determine the trend. Sousuke Amasakireceived the B.E. degree in Information and Computer Sciences from Okayama Prefectural University, Japan, in 2000 and the M.E. degree in Information and Computer Sciences from Graduate School of Information Science and Technology, Osaka University, Japan, in 2003. He has been in Ph.D. course of Graduate School of Information Science and Technology at Osaka University. His interests include the software process and the software quality assurance technique. He is a student member of IEEE and ACM. Takashi Yoshitomireceived the B.E. degree in Information and Computer Sciences from Osaka University, Japan, in 2002. He has been working for Hitachi Software Engineering Co., Ltd. Osamu Mizunoreceived the B.E., M.E., and Ph.D. degrees in Information and Computer Sciences from Osaka University, Japan, in 1996, 1998, and 2001, respectively. He is an Assistant Professor of the Graduate School of Information Science and Technology at Osaka University. His research interests include the improvement technique of the software process and the software risk management technique. He is a member of IEEE. Yasunari Takagireceived the B.E. degree in Information and Computer Science, from Nagoya Institute of Technology, Japan, in 1985. He has been working for OMRON Corporation. He has been also in Ph.D. course of Graduate School of Information Science and Technology at Osaka University since 2002. Tohru Kikunoreceived the B.E., M.Sc., and Ph.D. degrees in Electrical Engineering from Osaka University, Japan, in 1970, 1972, and 1975, respectively. He joined Hiroshima University from 1975 to 1987. Since 1990, he has been a Professor of the Department of Information and Computer Sciences at Osaka University. His research interests include the analysis and design of fault-tolerant systems, the quantitative evaluation of software development processes, and the design of procedures for testing communication protocols. He is a member of IEEE and ACM.     

Global Control of Robotic Highway Safety Markers: A Real-time Solution

This paper presents the design and implementation of a real-time solution for the global control of robotic highway safety markers. Problems addressed in the system are: (1) poor scalability and predictability as the number of markers increases, (2) jerky movement of markers, and (3) misidentification of safety markers caused by objects in the environment.An extensive analysis of the system and two solutions are offered: a basic solution and an enhanced solution. They are built respectively upon two task models: the periodic task model and the variable rate execution (VRE) task model. The former is characterized by four static parameters: phase, period, worst case execution time and relative deadline. The latter has similar parameters, but the parameter values are allowed to change at arbitrary times.The use of real-time tasks and scheduling techniques solve the first two problems. The third problem is solved using a refined Hough transform algorithm and a horizon scanning window. The approach decreases the time complexity of traditional implementations of the Hough transform with only slightly increased storage requirements.Supported, in part, by grants from the National Science Foundation (CCR-0208619 and CNS-0409382) and the National Academy of Sciences Transportation Research Board-NCHRP IDEA Program (Project #90).Jiazheng Shi received the B.E. and M.E. degrees in electrical engineering from Beijing University of Posts and Telecommunications in 1997 and 2000, respectively. In 2000, he worked with the Global Software Group, Motorola Inc. Currently, he is a Ph.D. candidate in the Computer Science and Engineering Department at the University of Nebraska–Lincoln. His research interests are automated human face recognition, image processing, computer vision, approximate theory, and linear system optimization.Steve Goddard is a J.D. Edwards Associate Professor in the Department of Computer Science & Engineering at the University of Nebraska–Lincoln. He received the B.A. degree in computer science and mathematics from the University of Minnesota (1985). He received the M.S. and Ph.D. degrees in computer science from the University of North Carolina at Chapel Hill (1995, 1998).His research interests are embedded, real-time and distributed systems with emphases in high assurance systems engineering and real-time, rate-based scheduling theory.Anagh Lal received a B.S. degree in Computer Science from the University of Mumbai (Bombay), Mumbai, in 2001. He is currently a graduate research assistant at the University of Nebraska–Lincoln working on a M.S. in Computer Science, and a member of the ConSystLab. His research interests lie in Databases, Constraint Processing and Real Time Systems. Anagh will be graduating soon and is looking for positions at research institutions.Jason Dumpert received a B.S. degree in electrical engineering from the University of Nebraska–Lincoln in 2001. He received a M.S. degree in electrical engineering from the University of Nebraska-Lincoln in 2004. He is currently a graduate research assistant at the University of Nebraska-Lincoln working on a Ph.D. in biomedical engineering. His research interests include mobile robotics and surgical robotics.Shane M. Farritor is an Associate Professor in the University of Nebraska–Lincolns Department of Mechanical Engineering. His research interests include space robotics, surgical robotics, biomedical sensors, and robotics for highway safety. He holds courtesy appointments in both the Department of Surgery and the Department of Orthopaedic Surgery at the University of Nebraska Medical Center, Omaha. He serves of both the AIAA Space Robotics and Automation technical committee and ASME Dynamic Systems and Control Robotics Panel. He received M.S. and Ph.D. degrees from M.I.T.     

Early Estimation of Users’ Perception of Software Quality

This paper presents a methodology for estimating users’ opinion of the quality of a software product. Users’ opinion changes with time as they progressively become more acquainted with the software product. In this paper, we study the dynamics of users’ opinion and offer a method for assessing users’ final perception, based on measurements in the early stages of product release. The paper also presents methods for collecting users’ opinion and from the derived data, shows how their initial belief state for the quality of the product is formed. It adapts aspects of Belief Revision theory in order to present a way of estimating users’ opinion, subsequently formed after their opinion revisions. This estimation is achieved by using the initial measurements and without having to conduct surveys frequently. It reports the correlation that users tend to infer among quality characteristics and represents this correlation through a determination of a set of constraints between the scores of each quality characteristic. Finally, this paper presents a fast and automated way of forming users’ new belief state for the quality of a product after examining their opinion revisions. Dimitris Stavrinoudis received his degree in Computer Engineering from Patras University and is a Ph.D. student of Computer Engineering and Informatics Department. He worked as a senior computer engineer and researcher at the R.A. Computer Technology Institute. He has participated in research and development projects in the areas of software engineering, databases and educational technologies. Currently, he works at the Hellenic Open University. His research interests include software quality, software metrics and measurements. Michalis Xenos received his degree and Ph.D. in Computer Engineering from Patras University. He is a Lecturer in the Informatics Department of the School of Sciences and Technology of the Hellenic Open University. He also works as a researcher in the Computer Technology Institute of Patras and has participated in over 15 research and development projects in the areas of software engineering and IT development management. His research interests include, inter alia, Software Engineering and Educational Technologies. He is the author of 6 books in Greek and over 30 papers in international journals and conferences. Pavlos Peppas received his B.Eng. in Computer Engineering from Patras University (1988), and his Ph.D. in Computer Science from Sydney University (1994). He joined Macquarie University, Sydney, as a lecturer in September 1993, and was promoted to a senior lecturer in October 1998. In January 2000, he took up an appointment at Intrasoft, Athens, where he worked as a senior specialist in the Data Warehousing department. He joint Athens Information Technology in February 2003 as a senior researcher, and since November 2003 he is an associate professor at the Dept of Business Administration at the University of Patras. He also holds an adjunct associate professorship at the School of Computer Science and Engineering at the University of New South Wales. His research interests lie primarily within the area of Artificial Intelligence, and more specifically in logic-based approaches to Knowledge Representation and Reasoning with application in robotics, software engineering, organizational knowledge management, and the semantic web. Dimitris Christodoulakis received his degree in Mathematics from the University of Athens and his Ph.D. in Informatics from the University of Bonn. He was a researcher at the National Informatics Centre of Germany. He is a Professor and Vice President of Computer Engineering and Informatics Department of Patras University. Scientific Coordinator in many research and development projects in the followings sections: Knowledge and Data Base Systems, Very large volume information storage, Hypertext, Natural Language Technology for Modern Greek. Author and co-author in many articles published in international conferences. Editor in proceedings of conventions. Responsible for proofing tools development for Microsoft Corp. He is Vice Director in the Research Academic Computer Technology Institute (RACTI).     

Handling non-canonical software requirements based on Annotated Predicate Calculus

Eliciting requirements for a proposed system inevitably involves the problem of handling undesirable information about customer's needs, including inconsistency, vagueness, redundancy, or incompleteness. We term the requirements statements involved in the undesirable information non-canonical software requirements. In this paper, we propose an approach to handling non-canonical software requirements based on Annotated Predicate Calculus (APC). Informally, by defining a special belief lattice appropriate for representing the stakeholder's belief in requirements statements, we construct a new form of APC to formalize requirements specifications. We then show how the APC can be employed to characterize non-canonical requirements. Finally, we show how the approach can be used to handle non-canonical requirements through a case study. Kedian Mu received B.Sc. degree in applied mathematics from Beijing Institute of Technology, Beijing, China, in 1997, M.Sc. degree in probability and mathematical statistics from Beijing Institute of Technology, Beijing, China, in 2000, and Ph.D. in applied mathematics from Peking University, Beijing, China, in 2003. From 2003 to 2005, he was a postdoctoral researcher at Institute of Computing Technology, Chinese Academy of Sciences, China. He is currently an assistant professor at School of Mathematical Sciences, Peking University, Beijing, China. His research interests include uncertain reasoning in artificial intelligence, knowledge engineering and science, and requirements engineering. Zhi Jin was awarded B.Sc. in computer science from Zhejiang University, Hangzhou, China, in 1984, and studied for her M.Sc. in computer science (expert system) and her Ph.D. in computer science (artificial intelligence) at National Defence University of Technology, Changsha, China. She was awarded Ph.D. in 1992. She is a senior member of China Computer Federation. She is currently a professor at Academy of Mathematics and System Sciences, Chinese Academy of Science. Her research interests include knowledge-based systems, artificial intelligence, requirements engineering, ontology engineering, etc. Her current research focuses on ontology-based requirements elicitation and analysis. She has got about 60 papers published, including co-authoring one book. Ruqian Lu is a professor of computer science of the Institute of Mathematics, Chinese Academy of Sciences. His research interests include artificial intelligence, knowledge engineering and knowledge based software engineering. He designed the “Tian Ma” software systems that have been widely applied in more than 20 fields, including the national defense and the economy. He has won two first class awards from Chinese Academy of Sciences and a National second class prize from the Ministry of Science and Technology. He has also won the sixth Hua Lookeng Prize for Mathematics. Yan Peng received B.Sc. degree in software from Jilin University, Changchun, China, in 1992. From June 2002 to December 2005, he studied for his M.E. in software engineering at College of Software Engineering, Graduate School of Chinese Academy of Sciences, Beijing, China. He was awarded M.E degree in 2006. He is currently responsible for CRM (customer relationship management) and BI (business intelligence) project in the BONG. His research interests include customer relationship management, business intelligence, data ming, software engineering and requirements engineering.     

The relationships among service quality,perceived value,customer satisfaction,and post-purchase intention in mobile value-added services

The purposes of this study are to construct an instrument to evaluate service quality of mobile value-added services and have a further discussion of the relationships among service quality, perceived value, customer satisfaction, and post-purchase intention. Structural equation modeling and multiple regression analysis were used to analyze the data collected from college and graduate students of 15 major universities in Taiwan. The main findings are as follows: (1) service quality positively influences both perceived value and customer satisfaction; (2) perceived value positively influences on both customer satisfaction and post-purchase intention; (3) customer satisfaction positively influences post-purchase intention; (4) service quality has an indirect positive influence on post-purchase intention through customer satisfaction or perceived value; (5) among the dimensions of service quality, “customer service and system reliability” is most influential on perceived value and customer satisfaction, and the influence of “content quality” ranks second; (6) the proposed model is proven with the effectiveness in explaining the relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile added-value services.     

An approach to discovering risks in development process of large and complex systems

When building a large and complex system, such as satellites, all sorts of risks have to be managed if it were to be successful. For risks in the design of an artifact, various reliability analysis techniques such as FTA or FMEA have been employed in the engineering domain. However, risks exist as well in the development process, and they could result in a failure of the system. In this paper, we present an approach to discovering risks in development process by collecting and organizing information produced during development process at low cost. We describe a prototype system called IDIMS, and show how it can be used to discover risks from e-mail communications between developers. The motivation of our work is to overcome thecapture bottleneck problem, and utilize now wasted information to improve development process. Yoshikiyo Kato: He received his B. Eng. (1998) and M.Eng. (2000) degrees in aeronautics and astronautics from The University of Tokyo. From September 1998 to July 1999, he was an exchange student at Department of Computer Science and Engineering of University of California, San Diego, and worked on software engineering tools. From May 2001 to July 2002, he was a research assistant at National Institute of Informatics (Japan). He is currently a Ph.D. student at Department of Advanced Interdisciplinary Studies of the University of Tokyo. His research interests include knowledge management, CSCW, HCI and software engineering He is a member of AAAI and JSAI. Takahiro Shirakawa: He received his B.Eng. (2000) and M.Eng. (2002) degrees in aeronautics and astronautics from the University of Tokyo. He is currently an assistant examiner at Japan Patent Office. Kohei Taketa: He received his B.Eng. (2000) and M.Eng. (2002) degrees in aeronautics and astronautics from the University of Tokyo. He is currently a software engineer at NTT Data Corp. Koichi Hori, Dr.Eng.: He received B.Eng, M.Eng, and Dr.Eng. degrees in electronic engineering from the University of Tokyo, Japan, in 1979, 1981, and 1984, respectively. In 1984, he joined National Institute of Japanese Literature where he developed AI systems for literature studies. Since 1988, he has been with the U University of Tokyo. He is currently a professor with Department of Advanced Interdisciplinary Studies, The University of Tokyo. From September 1989 to January 1990, he also held a visiting position at University of Compiegne, France. His current research interests include AI technology for supporting human creative activities, cognitive engineering, and Intelligent CAD systems. He is a member of IEEE, ACM, IEICE, IPSJ, JSAI, JSSST and JCSS.     

An Algebraic Hardware/Software Partitioning Algorithm

Hardware and software co-design is a design technique which delivers computer systems comprising hardware and software components.A critical phase of the co-design process is to decompose a program into hardware and software .This paper proposes an algebraic partitioning algorithm whose correctness is verified in program algebra.The authors inroduce a program analysis phase before program partitioning and deveop a collection of syntax-based splitting rules.The former provides the information for moving operations from software to hardware and reducing the interaction between compoents,and th latter supports a compositional approach to program partitioning.     

Preference update for e-commerce applications: Model,language, and processing

It is likely that customers issue requests based on out-of-date information in e-commerce application systems. Hence, the transaction failure rates would increase greatly. In this paper, we present a preference update model to address this problem. A preference update is an extended SQL update statement where a user can request the desired number of target data items by specifying multiple preferences. Moreover, the preference update allows easy extraction of criteria from a set of concurrent requests and, hence, optimal decisions for the data assignments can be made. We propose a group evaluation strategy for preference update processing in a multidatabase environment. The experimental results show that the group evaluation can effectively increase the customer satisfaction level with acceptable cost. Peng Li is the Chief Software Architect of didiom LLC. Before that, he was a visiting assistant professor of computer science department in Western Kentucky University. He received his Ph.D. degree of computer science from the University of Texas at Dallas. He also holds a B.Sc. and M.S. in Computer Science from the Renmin University of China. His research interests include database systems, database security, transaction processing, distributed and Internet computer and E-commerce. Manghui Tu received a Bachelor degree of Science from Wuhan University, P.R. China in 1996, and a Master Degree in Computer Science from the University of Texas at Dallas 2001. He is currently working toward the PhD degree in the Department of Computer Science at the University of Texas at Dallas. Mr. Tu’s research interests include distributed systems, grid computing, information security, mobile computing, and scientific computing. His PhD research work focus on the data management in secure and high performance data grid. He is a student member of the IEEE. I-Ling Yen received her BS degree from Tsing-Hua University, Taiwan, and her MS and PhD degrees in Computer Science from the University of Houston. She is currently an Associate Professor of Computer Science at the University of Texas at Dallas. Dr. Yen’s research interests include fault-tolerant computing, security systems and algorithms, distributed systems, Internet technologies, E-commerce, and self-stabilizing systems. She had published over 100 technical papers in these research areas and received many research awards from NSF, DOD, NASA, and several industry companies. She has served as Program Committee member for many conferences and Program Chair/Co-Chair for the IEEE Symposium on Application-Specific Software and System Engineering & Technology, IEEE High Assurance Systems Engineering Symposium, IEEE International Computer Software and Applications Conference, and IEEE International Symposium on Autonomous Decentralized Systems. She is a member of the IEEE. Zhonghang Xia received the B.S. degree in applied mathematics from Dalian University of Technology in 1990, the M.S. degree in Operations Research from Qufu Normal University in 1993, and the Ph.D. degree in computer science from the University of Texas at Dallas in 2004. He is now an assistant professor in the Department of Computer Science, Western Kentucky University, Bowling Green, KY. His research interests are in the area of multimedia computing and networking, distributed systems, and data mining.     

On evaluating the layout of UML diagrams for program comprehension

UML class and sequence diagrams are helpful for understanding the static structure and dynamic behavior of a software system. Algorithms and tools have been developed to generate these UML diagrams automatically for program understanding purposes. Many tools, however, often ignore perceptual factors in the layout of these diagrams. Therefore, users still have to spend much time and effort rearranging boxes and lines to make the diagram understandable. This article presents key criteria and guidelines for the effective layout of UML class and sequence diagrams from the perspective of perceptual theories. Two UML tools are evaluated to illustrate how the criteria can be applied to assess the readability of their generated diagrams. Kenny Wong is an Associate Professor in the Department of Computing Science at the University of Alberta. His main areas of research include software comprehension, evolution, and visualization. This research includes building, using, and evaluating integrated environments for reverse engineering, and devising strategies to understand and evolve diverse software systems. He is General Chair of the 2007 International Conference on Program Comprehension in Banff, and Program Chair of the 2008 International Conference on Software Maintenance in Beijing. Dabo Sun is an M.Sc. student in the Department of Computing Science at the University of Alberta. His research interests include program comprehension, software visualization, and end-user software engineering. He has assisted the teaching of courses in software engineering and web information systems. He also has been working as a software engineer on developing and maintaining industrial software systems.     

An efficient method for estimating null values in relational databases

Generally, a database system containing null value attributes will not operate properly. This study proposes an efficient and systematic approach for estimating null values in a relational database which utilizes clustering algorithms to cluster data, and a regression coefficient to determine the degree of influence between different attributes. Two databases are used to verify the proposed method: (1) Human resource database; and (2) Waugh's database. Furthermore, the mean of absolute error rate (MAER) and average error are used as evaluation criteria to compare the proposed method with other methods. It demonstrates that the proposed method is superior to existing methods for estimating null values in relational database systems. Jia-Wen Wang was born on September 5, 1978, in Taipei, Taiwan, Republic of China. She received the M.S. degree in information management from the National Yunlin University of Science and Technology, Yunlin, Taiwan, in 2003. Since 2003, she has been a PhD degree student in Information Management Department at the National Yunlin University of Science and Technology. Her current research interests include fuzzy systems, database systems, and artificial intelligence. Ching-Hsue Cheng received the B.S. degree in mathematics from Chinese Military Academy, Taiwan, in 1982, the M.S. degree in applied mathematics from the Chung Yuan Christian University, Taiwan, in 1988, and the Ph.D. degree in system engineering and management from National Defence University, Taiwan, in 1994. Currently, he is a professor of the Department of Information Management, National YunLin University of Technology & Science. His research interests are in decision science, soft computing, software reliability, performance evaluation, and fuzzy time series. He has published more than 120 refereed papers in these areas. He has been a principal investigator and project leader in a number of projects with government, and other research-sponsoring agencies.