1996/10/01 by V.R. Basili, Victor R. Basili, Lionel Briand +3 · 1,723 citations
Computer Science · #Computer science #Data mining #Object-oriented programming #Programming language #Quality (philosophy) #Set (abstract data type) #Software #Software Engineering Research #Software Engineering Techniques and Practices #Software Reliability and Analysis Research #Software development #Software development process #Software engineering #Software metric #Software quality #Suite #Systems development life cycle
paper · doi:10.1109/32.544352
published in IEEE Transactions on Software Engineering 22(10), 751-761 (IEEE Computer Society)
openalex publication_date 1996/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.