This paper presents the results of a study conducted at the University of Maryland in which we experimentally investigated the suite of Object-Oriented (OO) design metrics introduced by [Chidamber&Kemerer, 1994]. In order to do this, we assessed these metrics as predictors of fault-prone classes. This study is complementary to [Li&Henry, 1993] where the same suite of metrics had been used to assess frequencies of maintenance changes to classes. To perform our validation accurately, we collected data on the development of eight medium-sized information management systems based on identical requirements. All eight projects were developed using a sequential life cycle model, a well-known OO analysis/design method and the C++ programming language. Based on experimental results, the advantages and drawbacks of these OO metrics are discussed. Several of Chidamber&Kemerer's OO metrics appear to be useful to predict class fault-proneness during the early phases of the life-cycle. We ...

This paper proposes a comprehensive suite of measures to quantify the level of class coupling during the design of object-oriented systems. This suite takes into account the different OO design mechanisms provided by the C++ language (e.g., friendship between classes, specialization, and aggregation) but it can be tailored to other OO languages. The different measures in our suite thus reflect different hypotheses about the different mechanisms of coupling in OO systems. Based on actual project defect data, the hypotheses underlying our coupling measures are empirically validated by analyzing their relationship with the probability of fault detection across classes. The results demonstrate that some of these coupling measures may be useful early quality indicators of the design of OO systems. These measures are conceptually different from the OO design measures defined by Chidamber and Kemerer; in addition, our data suggests that they are complementary quality indicators.

Techniques for detecting defects in source code are fundamental to the success of any software development approach. A software development organization therefore needs to understand the utility of techniques such as reading or testing in its own environment. Controlled experiments have proven to be an effective means for evaluating software engineering techniques and gaining the necessary understanding about their utility. This paper presents a characterization scheme for controlled experiments that evaluate defect-detection techniques. The characterization scheme permits the comparison of results from similar experiments and establishes a context for crossexperiment analysis of those results. The characterization scheme is used to structure a detailed survey of four experiments that compared reading and testing techniques for detecting defects in source code. We encourage educators, researchers, and practition- Also with the Department of Computer Science, University of Kaiserslaut...

1 This paper describes an empirical comparison of several modeling techniques for predicting the quality of software components early in the software life cycle. Using software product measures, we built models that classify components as high-risk, i.e., likely to contain faults, or low-risk, i.e., likely to be free of faults. The modeling techniques evaluated in this study include principal component analysis, discriminant analysis, logistic regression, logical classification models, layered neural networks, and holographic networks. These techniques provide a good coverage of the main problemsolving paradigms: statistical analysis, machine learning, and neural networks. Using the results of independent testing, we determined the absolute worth of the predictive models and compare their performance in terms of misclassification errors, achieved quality, and verification cost. Data came from 27 software systems, developed and tested during three years of project-intensive academic cou...

Regression testing is an expensive testing process used to validate modified software and detect whether new faults have been introduced into previously tested code. To reduce the cost of regression testing, software testers may prioritize their test cases so that those which are more important, by some measure, are run earlier in the regression testing process. One goal of prioritization is to increase a test suite’s rate of fault detection. Previous empirical studies have shown that several prioritization techniques can significantly improve rate of fault detection, but these studies have also shown that the effectiveness of these techniques varies considerably across various attributes of the program, test suites, and modifications being considered. This variation makes it difficult for a practitioner to choose an appropriate prioritization technique for a given testing scenario. To address this problem, we analyze the fault detection rates that result from applying several different prioritization techniques to several programs and modified versions. The results of our analyses provide insights into what types of prioritization techniques are and are not appropriate under specific testing scenarios, and the conditions under which they are or are not appropriate. Our analysis approach can also be used by other researchers or practitioners to determine the prioritization techniques appropriate to other workloads. 1

In order to improve the quality of the software development process, we need to be able to build empirical multivariate models based on data collectable early in the software process. These models need to be both useful for prediction and easy to interpret, so that remedial actions may be taken in order to control and optimize the development process. We present an automated modeling technique which can be used as an alternative to regression techniques. We show how it can be used to facilitate the identification and aid the interpretation of the significant trends which characterize "high risk" components in several Ada systems. Finally, we evaluate the effectiveness of our technique based on a comparison with logistic regression based models. 1 Introduction It is often noted that a small number of software components are responsible for a large part of the difficulty during software development. In light of this relationship, there have been a number of studies that focus on the dev...

In this paper we present a concrete method for validating software product measures for internal attributes and provide guidelines for its application. This method integrates much of the relevant previous work, such as measurement theory, properties of measures, and GQM. We identify two types of validation: theoretical and empirical. The former addresses the question "is the measure measuring the attribute it is purporting to measure?", and the latter addresses the question "is the measure useful in the sense that it is related to other variables in expected ways?"

This paper uses the case study and its results to qualitatively compare our approach against current ad hoc practices used to improve existing measurement frameworks

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.

A formal model of program complexity developed earlier by the authors is used to derive evaluation criteria for program complexity measures. This is then used to determine which measures are appropriate within a particular application domain. A set of rules for determining feasible measures for a particular application domain are given, and an evaluation model for choosing among alternative feasible measures is presented. This model is used to select measures from the classification trees produced by the empirically guided software development environment of Selby and Porter, and early experiments show it to be an effective process.