Improving Credibility of Machine Learner Models in Software Engineering

Improving Credibility of Machine Learner Models in Software Engineering

Gary D. Boetticher (University of Houston – Clear Lake, USA)
Copyright: © 2007 |Pages: 21
DOI: 10.4018/978-1-59140-941-1.ch003
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Given a choice, software project managers frequently prefer traditional methods of making decisions rather than relying on empirical software engineering (empirical/machine learning- based models). One reason for this choice is the perceived lack of credibility associated with these models. To promote better empirical software engineering, a series of experiments are conducted on various NASA datasets to demonstrate the importance of assessing the ease/difficulty of a modeling situation. Each dataset is divided into three groups, a training set, and “nice/nasty” neighbor test sets. Using a nearest neighbor approach, “nice neighbors” align closest to same class training instances. “Nasty neighbors” align to the opposite class training instances. The “nice”, “nasty” experiments average 94% and 20%accuracy, respectively. Another set of experiments show how a ten-fold cross-validation is not sufficient in characterizing a dataset. Finally, a set of metric equations is proposed for improving the credibility assessment of empirical/machine learning models.

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Table of Contents
Du Zhang
Du Zhang
Chapter 1
J. J. Dolado, D. Rodríguez, J. Riquelme, F. Ferrer-Troyano, J. J. Cuadrado
One of the problems found in generic project databases, where the data is collected from different organizations, is the large disparity of its... Sample PDF
A Two-Stage Zone Regression Method for Global Characterization of a Project Database
Chapter 2
Marek Reformat, Petr Musilek, Efe Igbide
Amount of software engineering data gathered by software companies amplifies importance of tools and techniques dedicated to processing and analysis... Sample PDF
Intelligent Analysis of Software Maintenance Data
Chapter 3
Gary D. Boetticher
Given a choice, software project managers frequently prefer traditional methods of making decisions rather than relying on empirical software... Sample PDF
Improving Credibility of Machine Learner Models in Software Engineering
Chapter 4
Daniele Gunetti
Though inductive logic programming (ILP for short) should mean the “induction of logic programs”, most research and applications of this area are... Sample PDF
ILP Applications to Software Engineering
Chapter 5
Min Chen, Shu-Ching Chen
This chapter introduces an advanced content-based image retrieval (CBIR) system, MMIR, where Markov model mediator (MMM) and multiple instance... Sample PDF
MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework
Chapter 6
I-Ling Yen, Tong Gao
Reconfigurability is an important requirement in many application systems. Many approaches have been proposed to achieve static/dynamic... Sample PDF
A Genetic Algorithm-Based QoS Analysis Tool for Reconfigurable Service-Oriented Systems
Chapter 7
Witold Pedrycz, Giancarlo Succi
The learning abilities and high transparency are the two important and highly desirable features of any model of software quality. The transparency... Sample PDF
Fuzzy Logic Classifiers and Models in Quantitative Software Engineering
Chapter 8
Jelber Sayyad Shirabad, Timothy C. Lethbridge, Stan Matwin
This chapter presents the notion of relevance relations, an abstraction to represent relationships between software entities. Relevance relations... Sample PDF
Modeling Relevance Relations Using Machine Learning Techniques
Chapter 9
Yi Liu, Taghi M. Khoshgoftaar
A software quality estimation model is an important tool for a given software quality assurance initiative. Software quality classification models... Sample PDF
A Practical Software Quality Classification Model Using Genetic Programming
Chapter 10
Yan Ma, Lan Guo, Bojan Cukic
Accurate prediction of fault-prone modules in software development process enables effective discovery and identification of the defects. Such... Sample PDF
A Statistical Framework for the Prediction of Fault-Proneness
Chapter 11
Bhekisipho Twala, Michelle Cartwright, Martin Shepperd
Recently, the use of machine learning (ML) algorithms has proven to be of great practical value in solving a variety of software engineering... Sample PDF
Applying Rule Induction in Software Prediction
Chapter 12
Baowen Xu, Xiaoyuan Xie, Liang Shi, Changhai Nie
Genetic algorithms are a kind of global meta-heuristic search technique that searches intelligently for optimal solutions to a problem. Evolutionary... Sample PDF
Application of Genetic Algorithms in Software Testing
Chapter 13
Xudong He, Huiqun Yu, Yi Deng
Software has been a major enabling technology for advancing modern society, and is now an indispensable part of daily life. Because of the increased... Sample PDF
Formal Methods for Specifying and Analyzing Complex Software Systems
Chapter 14
Paul Dietz, Aswin van den Berg, Kevin Marth, Thomas Weigert, Frank Weil
Model-driven engineering proposes to develop software systems by first creating an executable model of the system design and then transforming this... Sample PDF
Practical Considerations in Automatic Code Generation
Chapter 15
Donghua Deng, Phillip C.Y. Sheu
This chapter presents a distributed proactive semantic software engineering environment (DPSSEE) that incorporates logic rules into a software... Sample PDF
DPSSEE: A Distributed Proactive Semantic Software Engineering Environment
Chapter 16
Shangping Ren, Jeffrey J.P. Tsai, Ophir Frieder
In this chapter, we present the role-based context constrained access control (RBCC) model. The model integrates contextual constraints specified in... Sample PDF
Adding Context into an Access Control Model for Computer Security Policy
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