Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

物件導向軟體之架構(Object-Oriented Software Construction)探討

Empirical validation of software metrics used to predict software quality attributes is important to ensure their practical relevance in software organizations. The aim of this work is to find the relation of object-oriented (OO) metrics with fault proneness at different severity levels of faults. For this purpose, different prediction models have been developed using regression and machine learning methods. We evaluate and compare the performance of these methods to find which method performs better at different severity levels of faults and empirically validate OO metrics given by Chidamber and Kemerer. The results of the empirical study are based on public domain NASA data set. The performance of the predicted models was evaluated using Receiver Operating Characteristic (ROC) analysis. The results show that the area under the curve (measured from the ROC analysis) of models predicted using high severity faults is low as compared with the area under the curve of the model predicted with respect to medium and low severity faults. However, the number of faults in the classes correctly classified by predicted models with respect to high severity faults is not low. This study also shows that the performance of machine learning methods is better than logistic regression method with respect to all the severities of faults. Based on the results, it is reasonable to claim that models targeted at different severity levels of faults could help for planning and executing testing by focusing resources on fault-prone parts of the design and code that are likely to cause serious failures.

Empirical validation of object-oriented metrics for predicting fault proneness models

For large software systems, the maintenance phase tends to have comparatively much longer duration than all the previous life-cycle phases taken together, obviously resulting in much more effort. A good measure of software maintainability can help better manage the maintenance phase effort. Software maintainability cannot be adequately measured by only source code or by documents. The readability and understandability of both source code and documentation should be considered to measure the maintainability. This paper proposes an integrated measure of software maintainability. The paper also proposes a new representation for rule base of fuzzy models, which require less space for storage and will be efficient in finding the results in the simulation. The proposed model measures the software maintainability based on three important aspects of software-readability of source code (RSC), documentation quality (DOQ), and understandability of software (UOS). Keeping in view the nature of these parameters, a fuzzy approach has been used to integrate these three aspects. A new efficient representation of rule base has been proposed for fuzzy models. This integrated measurement of software maintainability, which to our knowledge is a first attempt to quantify integrated maintainability, is bound to be better than any other single parameter maintainability measurement approach. Thus the output of this model can advise the software project managers in judging the maintenance efforts of the software.

An integrated measure of software maintainability

The increasing importance of software measurement has led to development of new software measures. Many metrics have been proposed related to various constructs like class, coupling, cohesion, inheritance, information hiding and polymorphism. But there is a little understanding of the empirical hypotheses and application of many of these measures. It is often difficult to determine which metric is more useful in which area. As a consequence, it is very difficult for project managers and practitioners to select measures for object-oriented systems. In this paper we investigate 22 metrics proposed by various researchers. The metrics are first defined and then explained using practical applications. They are applied on standard projects on the basis of which descriptive statistics, principal component analysis and correlation analysis is presented. Finally, a review of the empirical study concerning chosen metrics and subset of these measures that provide sufficient information is given and metrics providing overlapping information are excluded from the set.

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Empirical Study of Object-Oriented Metrics

The importance of software measurement is increasing, leading to the development of new measurement techniques. Many metrics have been proposed related to the various object-oriented (OO) constructs like class, coupling, cohesion, inheritance, information hiding and polymorphism. The purpose of this article is to explore relationships between the existing design metrics and probability of fault detection in classes. The study described here is a replication of an analogous study conducted by Briand et al. The aim is to provide empirical evidence to draw the strong conclusions across studies. We used the data collected from Java applications for constructing a prediction model. Results of this study show that many metrics capture the same dimensions in the metric set, hence are based on comparable ideas and provides redundant information. It is shown that by using a subset of metrics prediction models can be built to identify faulty classes. The model predicts faulty classes with more than 90p accuracy. The predicted model shows that import coupling and size metrics are strongly related to fault proneness, confirming the results from previous studies. However, there are also differences reported in this study with respect to previous studies such as inheritance metric which counts methods inherited in a class is also included in the predicted model. Copyright © 2008 John Wiley & Sons, Ltd.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/spip.389

Yogesh Singh

Papers

Empirical validation of object-oriented metrics for predicting fault proneness models

An integrated measure of software maintainability

Empirical Study of Object-Oriented Metrics

Empirical analysis for investigating the effect of object-oriented metrics on fault proneness: a replicated case study

An activation function adapting training algorithm for sigmoidal feedforward networks