The Confounding Effect of Class Size on The Validity of Obje(2)

de l’information

The Confounding Effect of Class Size on

The Validity of Object-Oriented Metrics

Khaled El Emam

National Research Council, Canada

Institute for Information Technology

Building M-50, Montreal Road

Ottawa, Ontario

Canada K1A OR6

khaled.el-emam@iit.nrc.caSaida BenlarbiNishith GoelCistel Technology210 Colonnade RoadSuite 204Nepean, OntarioCanada K2E 7L5{benlarbi, ngoel}@

Abstract

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 control for the potentially confounding effect of class

size. In this paper 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. After controlling for size none of the metrics we studied were

associated with fault-proneness anymore. This demonstrates a strong size confounding effect, and

casts doubt on the results of previous object-oriented metrics validation studies. It is recommended that

previous validation studies be re-examined to determine whether their conclusions would still hold after

controlling for size, and that future validation studies should always control for size.

1 Introduction

The validation of software product metrics has received much research attention by the software

engineering community. There are two types of validation that are recognized [48]: internal and external.

Internal validation is a theoretical exercise that ensures that the metric is a proper numerical

characterization of the property it claims to measure. External validation involves empirically

demonstrating that the product metric is associated with some important external metric (such as

measures of maintainability or reliability). These are also commonly referred to as theoretical and

empirical validation respectively [73], and procedures for achieving both are described in [15]. Our focus2in this paper is empirical validation.

Product metrics are of little value by themselves unless there is empirical evidence that they are

associated with important external attributes [65]. The demonstration of such a relationship can serve

two important purposes: early prediction/identification of high risk software components, and the

construction of preventative design and programming guidelines.1

Some authors distinguish between the terms ‘metric’ and ‘measure’ [2]. We use the term “metric” here to be consistent with

prevailing international standards. Specifically, ISO/IEC 9126:1991 [64] defines a “software quality metric” as a “quantitative scale

and method which can be used to determine the value a feature takes for a specific software product”.

21 Theoretical validations of many of the metrics that we consider in this paper can be found in [20][21][30].

百度搜索“77cn”或“免费范文网”即可找到本站免费阅读全部范文。收藏本站方便下次阅读，免费范文网，提供经典小说教育文库The Confounding Effect of Class Size on The Validity of Obje(2)在线全文阅读。