1. What have the authors contributed in "A validation of object-oriented design metrics as quality indicators*" ?
This paper presents the results of a study conducted at the University of Maryland in which the authors experimentally investigated the suite of Object-Oriented ( OO ) design metrics introduced by [ Chidamber & Kemerer, 1994 ].. 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.. Based on experimental results, the advantages and drawbacks of these OO metrics are discussed.. The authors also showed that they are, on their data set, better predictors than “ traditional ” code metrics, which can only be collected at a later phase of the software development processes.
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2. What are the future works mentioned in the paper "A validation of object-oriented design metrics as quality indicators*" ?
Their future work includes: • replicating this study in an industrial setting: a sample of large-scale projects developed in C++ and Ada95 in the framework of the NASA Goddard Flight Dynamics Division ( Software Engineering Laboratory ).. The authors believe that this drawback could be overcome by refining their data collection process in order to capture how much effort was spent on each class individually.. The fault-proneness prediction capabilities of the suite of OO metrics discussed in this paper can be different depending on the used programming language.
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3. What was used to extract Chidamber&Kemerer’s OO design metrics?
GEN++ [Devanbu, 1992] was used to extract Chidamber&Kemerer’s OO design metrics directly from the source code of the programs delivered at the end of the implementation phase.
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4. What is the way to evaluate the reliability of a multivariate logistic regression model?
In this case, a careful outlier analysis must be performed in order to make sure that the observed trend is not the result of a few observations [Dillon&Goldstein, 1984], even though logistic regression is deemed to be more robust for outliers than least-squares regression.
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![Table 8: Some differences and similarities between [Briand et al, 1994], [Li&Henry, 1993] and our work](/figures/table-8-some-differences-and-similarities-between-briand-et-2uun1496.webp)
