Screen LibraryMachine learning applications in software engineering /
Machine learning deals with the issue of how to build computer programs that improve their performance at some tasks through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering tur...
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| Corporate Authors: | |
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| Group Author: | ; |
| Published: |
World Scientific Pub. Co.,
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| Publisher Address: | Singapore ; Hackensack, N.J. : |
| Publication Dates: | 2005. |
| Literature type: | eBook |
| Language: | English |
| Subjects: | |
| Online Access: |
http://www.worldscientific.com/worldscibooks/10.1142/5700#t=toc |
| Summary: |
Machine learning deals with the issue of how to build computer programs that improve their performance at some tasks through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This book deals with the subject of machine learning applications in software engineering. It provides an overview of machine learning, summarizes the state-of-the-practice in this niche area, gives a classification of the existing work, and offers some application guidelines. Also included in the book is a collection of previously published papers in this research area. |
| Carrier Form: | 1 online resource (x,355pages) : illustrations, ports |
| Bibliography: | Includes bibliographical references. |
| ISBN: | 9789812569271 (electronic bk.) |
| CLC: | TP311.5 |
| Contents: | ch. 1. Introduction to machine learning and software engineering. 1.1. The challenge. 1.2. Overview of machine learning. 1.3. Learning approaches. 1.4. SE tasks for ML applications. 1.5. State-of-the-practice in ML&SE. 1.6. Status. 1.7. Applying ML algorithms to SE tasks. 1.8. Organization of the book -- ch. 2. ML applications in prediction and estimation. 2.1. Bayesian analysis of empirical software engineering cost models, (with S. Chulani, B. Boehm and B. Steece). 2.2. Machine learning approaches to estimating software development effort, (with K. Srinivasan and D. Fisher). 2.3. Estimating software project effort using analogies, (with M. Shepperd and C. Schofield). 2.4. A critique of software defect prediction models, (with N. E. Fenton and M. Neil). 2.5. Using regression trees to classify fault-prone software modules, (with T. M. Khoshgoftaar, E. B. Allen and J. Deng). 2.6. Can genetic programming improve software effort estimation? A comparative evaluation, (with C. J. Burgess and M. Lefley). 2.7. Optimal software release scheduling based on artificial neural networks, (with T. Dohi, Y. Nishio, and S. Osaki) -- ch. 3. ML applications in property and model discovery. 3.1. Identifying objects in procedural programs using clustering neural networks, (with S. K. Abd-El-Hafiz). 3.2. Bayesian-learning based guidelines to determine equivalent mutants, (with A. M. R. Vincenzi, et al.) -- ch. 4. ML applications in transformation. 4.1. Using neural networks to modularize software, (with R. Schwanke and S. J. Hanson) -- ch. 5. ML applications in generation and synthesis. 5.1. Generating software test data by evolution, (with C. C. Michael, G. McGraw and M. A. Schatz) -- ch. 6. ML applications in reuse. 6.1. On the reuse of software : a case-based approach employing a repository, (with P. Katalagarianos and Y. Vassiliou) -- ch. 7. ML applications in requirement acquisition. 7.1. Inductive specification recovery : understanding software by learning from example behaviors, (with W. W. Cohen). 7.2. Explanation-based scenario generation for reactive system models, (with R. J. Hall) -- ch. 8. ML applications in management of development knowledge. 8.1. Case-based knowledge management tools for software development, (with S. Henninger) -- ch. 9. Guidelines and conclusion. |