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Keynote at the SPA Software in Practice, London, 26 June 2019. Agile methods were aimed at small, co-located teams developing non-critical software products. The success of these methods for small teams have led to use in projects with tens of teams and hundreds of developers. Are agile methods suited in this new context? What fundamental assumptions in agile methods become challenging with scale? What can we learn from prior studies on key areas such as managing uncertainty, coordination, sharing knowledge, self management and tailoring of development method?

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A bit of a retrospective. Back in the spring of 2005, I delivered this presentation at a Defense Software Symposium. The idea was that if we manage the knowledge behind a software system properly we can create, integrate, manage, and evolve that software far more effectively than we have in the past. This discussion proceeded with reference to very large and very complex software engineering and integration projects.

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Software metrics are increasingly playing a central role in the planning and control of software development projects. Coupling measures have important applications in software development and maintenance. Existing literature on software metrics is mainly focused on centralized systems, while work in the area of distributed systems, particularly in service-oriented systems, is scarce. Distributed systems with service oriented components are even more heterogeneous networking and execution environment. Traditional coupling measures take into account only “static” couplings. They do not account for “dynamic” couplings due to polymorphism and may significantly underestimate the complexity of software and misjudge the need for code inspection, testing and debugging. This is expected to result in poor predictive accuracy of the quality models in distributed Object Oriented systems that utilize static coupling measurements. In order to overcome these issues, we propose a hybrid model in Distributed Object Oriented Software for measure the coupling dynamically. In the proposed method, there are three steps such as Instrumentation process, Post processing and Coupling measurement. Initially the instrumentation process is done. In this process the instrumented JVM that has been modified to trace method calls. During this process, three trace files are created namely .prf, .clp, .svp. In the second step, the information in these file are merged. At the end of this step, the merged detailed trace of each JVM contains pointers to the merged trace files of the other JVM such that the path of every remote call from the client to the server can be uniquely identified. Finally, the coupling metrics are measured dynamically. The implementation results show that the proposed system will effectively measure the coupling metrics dynamically.

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Agent Assisted Methodologies have become an important subject of research in advance Software Engineering. Several methodologies have been proposed as, a theoretical approach, to facilitate and support the development of complex distributed systems. An important question when facing the construction of Agent Applications is deciding which methodology to follow. Trying to answer this question, a framework with several criteria is applied in this paper for the comparative analysis of existing multiagent system methodologies. The results of the comparative over two of them, conclude that those methodologies have not reached a sufficient maturity level to be used by the software industry. The framework has also proved its utility for the evaluation of any kind of Agent Assisted Software Engineering Methodology.

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Journal of Management Information Systems / Summer 2014, Vol. 31, No. 1, pp. 79–106. © 2014 M.E. Sharpe, Inc. All rights reserved. Permissions: www.copyright.com ISSN 0742–1222 (print) / ISSN 1557–928X (online) DOI: 10.2753/MIS0742-1222310104 Achieving IT Program Goals with Integrative Conflict Management JAMES J. JIANG, JAMIE Y.T. CHANG, HOUN-GEE CHEN, ERIC T.G. WANG, AND GARY KLEIN James J. Jiang is the Fu-Bon Chair Professor, College of Management, National Taiwan University (NTU), Taiwan. Prior to joining NTU he was a distinguished professor of information systems at the Australian National University and professor of information systems at the University of Central Florida. He earned his Ph.D. in information systems from the University of Cincinnati. Dr. Jiang’s research interests include IS project and program management and IT service quality. He has published over 160 academic journal articles related to these subjects. He is an associate editor of Infor- mation & Management and Journal of the Association for Information Systems and a senior editor of MIS Quarterly. Jamie Y.T. Chang is an assistant professor of information management at Tunghai Uni- versity, Taiwan. She received her Ph.D. in information management from the National Central University, Taiwan. Her research interests include IS project management, IS program management, and enterprise systems implementation. Her current research projects involve the development of program goal consensus theory. Dr. Chang’s work has been published in International Journal of Project Management, Journal of Systems and Software, and MIS Review. houn-gee Chen is associate dean and professor of business administration in the College of Management, National Taiwan University, Taiwan. He earned his Ph.D. in industrial engineering from the University of Wisconsin–Madison. He was previously a faculty member at the University of Notre Dame and National Tsing Hua University. His research interests include e-commerce, management information systems, information technology, project management, and software quality. Dr. Chen’s research has been published in Journal of Management Information Systems, Information & Management, Decision Sciences, Communications of the ACM, IEEE Transactions on Professional Communication, and others. He is the editor-in-chief of the Journal of Information Management and serves on the editorial boards of many international journals. eriC T.g. Wang is Information Management Chaired Professor in the Department of Information Management at National Central University, Taiwan. He received a Ph.D. in business administration, specialized in computer and information systems, from the William E. Simon Graduate School of Business Administration, Univer- sity of Rochester. His research interests include electronic commerce, supply chain management, outsourcing, organizational economics, and organizational impact of information technology. Hi ...

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Algorithm Example For the following task: Use the random module to write a number guessing game. The number the computer chooses should change each time you run the program. Repeatedly ask the user for a number. If the number is different from the computer's let the user know if they guessed too high or too low. If the number matches the computer's, the user wins. Keep track of the number of tries it takes the user to guess it. An appropriate algorithm might be: Import the random module Display a welcome message to the user Choose a random number between 1 and 100 Get a guess from the user Set a number of tries to 0 As long as their guess isn’t the number Check if guess is lower than computer If so, print a lower message. Otherwise, is it higher? If so, print a higher message. Get another guess Increment the tries Repeat When they guess the computer's number, display the number and their tries count Notice that each line in the algorithm corresponds to roughly a line of code in Python, but there is no coding itself in the algorithm. Rather the algorithm lays out what needs to happen step by step to achieve the program. Software Quality Metrics for Object-Oriented Environments AUTHORS: Dr. Linda H. Rosenberg Lawrence E. Hyatt Unisys Government Systems Software Assurance Technology Center Goddard Space Flight Center Goddard Space Flight Center Bld 6 Code 300.1 Bld 6 Code 302 Greenbelt, MD 20771 USA Greenbelt, MD 20771 USA I. INTRODUCTION Object-oriented design and development are popular concepts in today’s software development environment. They are often heralded as the silver bullet for solving software problems. While in reality there is no silver bullet, object-oriented development has proved its value for systems that must be maintained and modified. Object-oriented software development requires a different approach from more traditional functional decomposition and data flow development methods. This includes the software metrics used to evaluate object-oriented software. The concepts of software metrics are well established, and many metrics relating to product quality have been developed and used. With object-oriented analysis and design methodologies gaining popularity, it is time to start investigating object-oriented metrics with respect to software quality. We are interested in the answer to the following questions: • What concepts and structures in object-oriented design affect the quality of the software? • Can traditional metrics measure the critical object-oriented structures? • If so, are the threshold values for the metrics the same for object-oriented designs as for functional/data designs? • Which of the many new metrics found in the literature are useful to measure the critical concepts of object-oriented structures? II. METRIC EVALUATION CRITERIA While metrics for the traditional functional decomposition and data analysis design appro ...

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Software project estimation is important for allocating resources and planning a reasonable work schedule. Estimation models are typically built using data from completed projects. While organizations have their historical data repositories, it is difficult to obtain their collaboration due to privacy and competitive concerns. To overcome the issue of public access to private data repositories this study proposes an algorithm to extract sufficient data from the GitHub repository for building duration estimation models. More specifically, this study extracts and analyses historical data on WordPress projects to estimate OSS project duration using commits as an independent variable as well as an improved classification of contributors based on the number of active days for each contributor within a release period. The results indicate that duration estimation models using data from OSS repositories perform well and partially solves the problem of lack of data encountered in empirical research in software engineering.

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