Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects (Advances in Intelligent Information Technologies (Aiit) Book Series)

371 Pages·2009·12.35 MB·English

by  Farid Meziane

Artificial Intelligence
Applications for Improved
Software Engineering
Development:
New Prospects
Farid Meziane
University of Salford, UK
Sunil Vadera
University of Salford, UK
InformatIon scIence reference
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Library of Congress Cataloging-in-Publication Data
Artificial intelligence applications for improved software engineering development : new prospects / Farid Meziane and Sunil
Vadera, editors.
p. cm.
Includes bibliographical references and index.
Summary: "This book provides an overview of useful techniques in artificial intelligence for future software development
along with critical assessment for further advancement"--Provided by publisher.
ISBN 978-1-60566-758-4 (hardcover) -- ISBN 978-1-60566-759-1 (ebook) 1. Computer software--Development. 2. Artificial
intelligence. I. Meziane, Farid. II. Vadera, Sunil.
QA76.76.D47A82 2009
006.3--dc22
2009018603
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the
authors, but not necessarily of the publisher.
Editorial Advisory Board
Epaminondas Kapetanios, University of Westminster, UK
Leonid Kof, Munich Technical University, Germany
C. Peng Lam, Edith Cowan University, Australia
David Lo, Singapore Management University, Singapore
Elisabeth Metais, CNAM, Paris, France
Dilip Patel, London South Bank University, UK
Vijayan Sugumaran, Oakland University, USA
List of Reviewers
Adil Al Yasiri, University of Salford, UK
Chris Bryant, University of Salford, UK
Epaminondas Kapetanios, University of Westminster, UK
Zoubida Kedad, Université de Versailles, France
Leonid Kof, Munich Technical University, Germany
C. Peng Lam, Edith Cowan University, Australia
David Lo, Singapore Management University, Singapore
Elisabeth Metais, CNAM, Paris, France
Edwin Mit, University Malaysia Sarawak, Malaysia
Rafael Munoz, Universidat de Alicante, Spain
Dilip Patel, London South Bank University, UK
Muthu Ramachandran, Leeds Metropolitan University, UK
Vijay Sugumaran, Oakland University, USA
Krishnaprasad Thirunarayan, Wright State University, USA
Jia Wu, East Lancs Moneyline (IPS) Ltd, UK
Table of Contents
Foreword ............................................................................................................................................xiii
Preface .................................................................................................................................................xv
Acknowledgment ..............................................................................................................................xxii
Section 1
Project Management and Cost Estimation
Chapter 1
Software Project and Quality Modelling Using Bayesian Networks .....................................................1
Norman Fenton, Queen Mary, University of London, United Kingdom
Peter Hearty, Queen Mary, University of London, United Kingdom
Martin Neil, Queen Mary, University of London, United Kingdom
Łukasz Radliński, Queen Mary, University of London, United Kingdom, and
University of Szczecin, Poland
Chapter 2
Using Bayesian Networks for Web Effort Estimation .........................................................................26
Emilia Mendes, The University of Auckland, New Zealand
Chapter 3
Optimizing Software Development Cost Estimates using Multi-Objective Particle
Swarm Optimization ............................................................................................................................45
Tad Gonsalves, Sophia University, Japan
Kei Yamagishi, Sophia University, Japan
Ryo Kawabata, Sophia University, Japan
Kiyoshi Itoh, Sophia University, Japan
Chapter 4
Auto-Associative Neural Networks to Improve the Accuracy of Estimation Models .........................66
Salvatore A. Sarcia, Università di Roma Tor Vergata, Italy
Giovanni Cantone, University of Maryland, USA
Victor R. Basili, University of Maryland, USA
Section 2
Requirements Engineering and Specification
Chapter 5
From Textual Scenarios to Message Sequence Charts .........................................................................83
Leonid Kof, Technische Universität München, Germany
Chapter 6
A Bayesian Network for Predicting the Need for a Requirements Review .......................................106
José del Sagrado Martínez, University of Almería, Spain
Isabel María del Águila Cano, University of Almería, Spain
Chapter 7
Knowledge Engineering Support for Software Requirements, Architectures and Components .......129
Muthu Ramachandran, Leeds Metropolitan University, UK
Chapter 8
MUSTER: A Situational Tool for Requirements Elicitation ..............................................................146
Chad Coulin, University of Technology Sydney, Australia & LAAS CNRS, France
Didar Zowghi, University of Technology Sydney, Australia
Abd-El-Kader Sahraoui, LAAS CNRS, France
Section 3
Software Design and Implementation
Chapter 9
An Intelligent Computational Argumentation System for Supporting Collaborative Software
Development Decision Making .........................................................................................................167
Xiaoqing (Frank) Liu, Missouri University of Science and Technology, USA
Ekta Khudkhudia, Missouri University of Science and Technology, USA
Lei Wen, Missouri University of Science and Technology, USA
Vamshi Sajja, Missouri University of Science and Technology, USA
Ming C. Leu, Missouri University of Science and Technology, USA
Chapter 10
Supporting Quality-Driven Software Design through Intelligent Assistants .....................................181
Alvaro Soria, ISISTAN Research Institute and CONICET, Argentina
J. Andres Diaz-Pace, Software Engineering Institute, USA
Len Bass, Software Engineering Institute, USA
Felix Bachmann, Software Engineering Institute, USA
Marcelo Campo, ISISTAN Research Institute and CONICET, Argentina
Section 4
Software Testing and Maintenance
Chapter 11
Constraint-Based Techniques for Software Testing ...........................................................................218
Nikolai Kosmatov, CEA LIST, Software Safety Laboratory, France
Chapter 12
Computational Intelligence for Functional Testing ...........................................................................233
C. Peng Lam, Edith Cowan University, Australia
Chapter 13
Mining Past-Time Temporal Rules: A Dynamic Analysis Approach .................................................259
David Lo, Singapore Management University, Singapore
Siau-Cheng Khoo, National University of Singapore, Singapore
Chao Liu, Microsoft Research – Redmond, USA
Chapter 14
Artificial Intelligence in Software Engineering: Current Developments and Future Prospects ........278
Farid Meziane, University of Salford, UK
Sunil Vadera, University of Salford, UK
Compilation of References ...............................................................................................................300
About the Contributors ....................................................................................................................336
Index ...................................................................................................................................................343
Detailed Table of Contents
Foreword ............................................................................................................................................xiii
Preface .................................................................................................................................................xv
Acknowledgment ..............................................................................................................................xxii
Section 1
Project Management and Cost Estimation
Chapter 1
Software Project and Quality Modelling Using Bayesian Networks .....................................................1
Norman Fenton, Queen Mary, University of London, United Kingdom
Peter Hearty, Queen Mary, University of London, United Kingdom
Martin Neil, Queen Mary, University of London, United Kingdom
Łukasz Radliński, Queen Mary, University of London, United Kingdom, and
University of Szczecin, Poland
Chapter 1 provides an introduction to the use of Bayesian Network (BN) models in Software Engineering.
A short overview of the theory of BNs is included, together with an explanation of why BNs are ideally
suited to dealing with the characteristics and shortcomings of typical software development environ-
ments. This theory is supplemented and illustrated using real world models that illustrate the advantages
of BNs in dealing with uncertainty, causal reasoning and learning in the presence of limited data.
Chapter 2
Using Bayesian Networks for Web Effort Estimation .........................................................................26
Emilia Mendes, The University of Auckland, New Zealand
Web effort models and techniques provide the means for Web companies to formalise the way they
estimate effort for their projects, and help in obtaining more accurate estimates. Accurate estimates are
fundamental to help project managers allocate resources more adequately, thus supporting projects to be
finished on time and within budget. The aim of this chapter is to introduce the concepts related to Web
effort estimation and effort forecasting techniques, and to discuss effort prediction when the estimation
technique used is Bayesian Networks.
Chapter 3
Optimizing Software Development Cost Estimates using Multi-Objective Particle
Swarm Optimization ............................................................................................................................45
Tad Gonsalves, Sophia University, Japan
Kei Yamagishi, Sophia University, Japan
Ryo Kawabata, Sophia University, Japan
Kiyoshi Itoh, Sophia University, Japan
Chapter 3 shows how the recently developed Computational Intelligence techniques can effectively
improve the prediction power of existing models. In particular, the authors focus on the adaptation of
the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm in simultaneously minimizing
two objective functions – prediction error rate and model complexity. This provides the project manager
with an opportunity to choose from a set of optimal solutions represented in a trade-off relationship
on the Pareto front. Traditional search algorithms use knowledge of the terrain and expert heuristics to
guide the search; such uses make them problem-specific and domain-dependent. The MOPSO meta-
heuristic approach relies neither on the knowledge of the problem nor on the experts’ heuristics, making
its application wide and extensive.
Chapter 4
Auto-Associative Neural Networks to Improve the Accuracy of Estimation Models .........................66
Salvatore A. Sarcia, Università di Roma Tor Vergata, Italy
Giovanni Cantone, University of Maryland, USA
Victor R. Basili, University of Maryland, USA
In Chapter 4, the authors focus on improving regression models by decreasing their redundancy and
increasing their parsimony, i.e., they turn the model into a model with fewer variables than the former.
They present an empirical auto-associative neural network-based strategy for model improvement, which
implements a reduction technique called Curvilinear component analysis. The contribution of this chapter
is to show how multi-layer feedforward neural networks can be a useful and practical mechanism for
improving software engineering estimation models.
Section 2
Requirements Engineering and Specification
Chapter 5
From Textual Scenarios to Message Sequence Charts .........................................................................83
Leonid Kof, Technische Universität München, Germany
Chapter 5 presents an approach that analyzes textual scenarios with the means of computational linguistics,
identifies where actors or whole actions are missing from the text, completes the missing information,
and creates a message sequence chart (MSC) including the information missing from the textual scenario.
Finally, this MSC is presented to the requirements analyst for validation. The book chapter presents
also a case study where scenarios from a requirement document based on industrial specifications were
translated to MSCs. The case study shows feasibility of the approach.
Chapter 6
A Bayesian Network for Predicting the Need for a Requirements Review .......................................106
José del Sagrado Martínez, University of Almería, Spain
Isabel María del Águila Cano, University of Almería, Spain
Chapter 6 focuses on the construction of “Requisites”, a Bayesian network designed to be used as a
predictor that tells us whether a requirements specification has enough quality to be considered as a
baseline. Requisites have been defined using several information sources, such as standards and reports,
and through interaction with experts, in order to structure and quantify the final model. This Bayesian
network reflects the knowledge needed when assessing a requirements specification. The authors show
how Requisites can be used through the study of some use cases. After the propagation over the network
of information collected about the certainty of a subset of variables, the value predicted will determine
if the requirements specification has to be revised.
Chapter 7
Knowledge Engineering Support for Software Requirements, Architectures and Components .......129
Muthu Ramachandran, Leeds Metropolitan University, UK
Chapter 7 provides insights into the application of knowledge based approaches to the development of
agile software development, software product line, software components and architecture. In particular,
it presents three research systems that demonstrate the potential benefits of utilising knowledge based
approaches to support agile methods. The first system, called SoBA, supports the use of a story board
for agile software development; the second system, called .NET designer, provides design rationale for
choosing appropriate architectural solutions, and the third system, called RAIS, provides reuse assess-
ment and improvement for designing reusable software components.
Chapter 8
MUSTER: A Situational Tool for Requirements Elicitation ..............................................................146
Chad Coulin, University of Technology Sydney, Australia & LAAS CNRS, France
Didar Zowghi, University of Technology Sydney, Australia
Abd-El-Kader Sahraoui, LAAS CNRS, France
Chapter 8 presents a collaborative and situational tool called MUSTER, that has been specifically designed
and developed for requirements elicitation workshops, and which utilizes, extends, and demonstrates a
successful application of intelligent technologies for Computer Aided Software Engineering and Com-
puter Aided Method Engineering. The primary objective of this tool is to improve the effectiveness and
efficiency of the requirements elicitation process for software systems development, whilst addressing
some of the common issues often encountered in practice through the integration of intelligent technolo-
gies. The tool also offers an example of how a group support system, coupled with artificial intelligence,
can be applied to very practical activities and situations within the software development process.