C4.5: Programs for Machine Learning (書評)

Much effort has been devoted to the development and empirical validation of object-oriented metrics. The empirical validations performed thus far would suggest that a core set of validated metrics is close to being identified. However, none of these studies allow for the potentially confounding effect of class size. We demonstrate a strong size confounding effect and question the results of previous object-oriented metrics validation studies. We first investigated whether there is a confounding effect of class size in validation studies of object-oriented metrics and show that, based on previous work, there is reason to believe that such an effect exists. We then describe a detailed empirical methodology for identifying those effects. Finally, we perform a study on a large C++ telecommunications framework to examine if size is really a confounder. This study considered the Chidamber and Kemerer metrics and a subset of the Lorenz and Kidd metrics. The dependent variable was the incidence of a fault attributable to a field failure (fault-proneness of a class). Our findings indicate that, before controlling for size, the results are very similar to previous studies. The metrics that are expected to be validated are indeed associated with fault-proneness.

The confounding effect of class size on the validity of object-oriented metrics

To make recommendations for improving requirements engineering processes, it is critical to understand the problems faced in contemporary practice. We describe a field study whose general objectives were to formulate recommendations to practitioners for improving requirements engineering processes, and to provide directions for future research on methods and tools. The results indicate that there are seven key issues of greatest concern in requirements engineering practice. These issues are discussed in terms of the problems they represent, how these problems are addressed successfully in practice, and impediments to the implementation of such good practices.

A field study of requirements engineering practices in information systems development

For 15 years, the Software Engineering Laboratory (SEL) has been carrying out studies and experiments for the purpose of understand- ing, assessing, and improving software and software processes within a production software development environment at the National Aeronautics and Space Administration/Goddard Space Flight Center (NASA/GSFC). The SEL comprises three major organizations: NASA/GSFC, Flight Dynamics Division University of Maryland, Department of Computer Science Computer Sciences Corporation, Flight Dynamics Technology Group - These organizations have jointly carried out several hundred software studies, producing hundreds of reports, papers, and documents, all of which de scribe some aspect of the software engineering technology that has been analyzed in the flight dynamics environment at NASA. The studies range from small, controlled experiments (such as analyzing the effectiveness of code readingversus that of functional testing) tolarge, multiple- project studies (such as assessing the impacts of Ada on a production environment). The organization's driving goal is to improve the software process continually, so that sustained improvement may be observed in the resulting products. This paper discusses the SEL as a functioning example of an operational software experience factory and summarizes the characteristics of and major lessons learned from 15 years of SEL operations.

The software engineering laboratory: an operational software experience factory

Comparing case-based reasoning classifiers for predicting high risk software components

In this paper we characterize and model the cost of rework in a Component Factory (CF) organization. A CF is responsible for developing and packaging reusable software components. Data was collected on corrective maintenance activities for the Generalized Support Software reuse asset library located at the Flight Dynamics Division of NASA's GSFC. We then constructed a predictive model of the cost of rework using the C4.5 system for generating a logical classification model. The predictor variables for the model are measures of internal software product attributes. The model demonstrates good prediction accuracy, and can be used by managers to allocate resources for corrective maintenance activities. Furthermore, we used the model to generate proscriptive coding guidelines to improve programming, practices so that the cost of rework can be reduced in the future. The general approach we have used is applicable to other environments.

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Characterizing and modeling the cost of rework in a library of reusable software components

This document presents the concepts and architecture of the Software Management Environment (SME), developed for the Software Engineering Branch of the Flight Dynamic Division (FDD) of GSFC. The SME provides an integrated set of experience-based management tools that can assist software development managers in managing and planning flight dynamics software development projects. This document provides a high-level description of the types of information required to implement such an automated management tool.

Software Management Environment (SME) concepts and architecture, revision 1

Over 50 individual Software Engineering Laboratory (SEL) research results, extracted from a review of published SEL documentation, that can be applied directly to managing software development projects are captured. Four basic categories of results are defined and discussed - environment profiles, relationships, models, and management rules. In each category, research results are presented as a single page that summarizes the individual result, lists potential uses of the result by managers, and references the original SEL documentation where the result was found. The document serves as a concise reference summary of applicable research for SEL managers.

Software Engineering Laboratory (SEL) relationships, models, and management rules

This document presents the components and algorithms of the Software Management Environment (SME), a management tool developed for the Software Engineering Branch (Code 552) of the Flight Dynamics Division (FDD) of the Goddard Space Flight Center (GSFC). The SME provides an integrated set of visually oriented experienced-based tools that can assist software development managers in managing and planning software development projects. This document describes and illustrates the analysis functions that underlie the SME's project monitoring, estimation, and planning tools. 'SME Components and Algorithms' is a companion reference to 'SME Concepts and Architecture' and 'Software Engineering Laboratory (SEL) Relationships, Models, and Management Rules.'

Robert Hendrick

Papers

Characterizing and modeling the cost of rework in a library of reusable software components

Software Management Environment (SME) concepts and architecture, revision 1

Software Engineering Laboratory (SEL) relationships, models, and management rules

Software Management Environment (SME): Components and algorithms

Characterizing and Modeling the Cost of Rework in a Library of Reusable Software Components