A validation of object-oriented design metrics as quality indicators

• Published in: IEEE transactions on software engineering Vol. 22; no. 10; pp. 751 - 761
• Main Authors: Basili, V.R., Briand, L.C., Melo, W.L.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.1996; Institute of Electrical and Electronics Engineers; IEEE Computer Society
• Subjects: Applied sciences; C language; C plus plus; Computer languages; Computer science; control theory; systems; Costs; Dependent variables; Design methodology; Design techniques; Exact sciences and technology; Information management; Language processing and microprogramming; Object oriented modeling; Object oriented programming; Predictive models; Programming; Programming languages; Quality control; Resource management; Software; Software development; Software engineering; Software maintenance; Software systems; Studies; System testing; Systems development; Validation; Life cycle; Errors; Software development; Prediction; Computer programming languages; Metric; C++ language; Object oriented programming
• ISSN: 0098-5589; 1939-3520
• DOI: 10.1109/32.544352

This paper presents the results of a study in which we empirically investigated the suite of object-oriented (OO) design metrics introduced in (Chidamber and Kemerer, 1994). More specifically, our goal is to assess these metrics as predictors of fault-prone classes and, therefore, determine whether they can be used as early quality indicators. This study is complementary to the work described in (Li and 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 empirical and quantitative analysis, the advantages and drawbacks of these OO metrics are discussed. Several of Chidamber and Kemerer's OO metrics appear to be useful to predict class fault-proneness during the early phases of the life-cycle. Also, on our data set, they are better predictors than "traditional" code metrics, which can only be collected at a later phase of the software development processes.