A Metrics Suite for Object Oriented Design

By Shyam R. Chidamber, Chris F. Kemerer. IEEE Trans. Software Eng., 2015

2,599 citations recorded by the source corpus.

Given the central role that software development plays in the delivery and application of information technology, managers are increasingly focusing on process improvement in the software development area. This demand has spurred the provision of a number of new and/or improved approaches to software development, with perhaps the most prominent being object-orientation (OO). In addition, the focus on process improvement has increased the demand for software measures, or metrics with which to manage the process. The need for such metrics is particularly acute when an organization is adopting a new technology for which established practices have yet to be developed. This research addresses these needs through the development and implementation of a new suite of metrics for OO design. Metrics developed in previous research, while contributing to the field's understanding of software development processes, have generally been subject to serious criticisms, including the lack of a theoretical base. Following Wand and Weber (1989), the theoretical base chosen for the metrics was the ontology of Bunge (1977). Six design metrics are developed, and then analytically evaluated against Weyuker's (1988) proposed set of measurement principles. An automated data collection tool was then developed and implemented to collect an empirical sample of these metrics at two field sites in order to demonstrate their feasibility and suggest ways in which managers may use these metrics for process improvement. >

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/b1f3f05449b0f922484c4909dd37bb131098eab5

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Citations in the corpus

Dynamic coupling measurement for object-oriented software (2004, neutral): “A large body of work exists on the static measurement of cohesion and coupling, both for procedural [25] and objectoriented systems [15], [24].”
The DaCapo benchmarks: java benchmarking development and analysis (2006, positive): “1 Code Complexity To measure the complexity of the benchmark code, we use the Chidamber and Kemerer object-oriented programming (CK) metrics [12] measured with the ckjm software package [36].”
Evolving software product lines with aspects (2008, positive): “The metrics for coupling, cohesion, and size were defined based on classic OO metrics [8]; the original metrics definitions were extended to be applied in a paradigm-independent way, supporting the generation of comparable results.”
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults (2006, neutral): “Similar results were found in [6], [9], and [14].”
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process (2009, neutral): “Structural object-orientation (OO) measurements, such as those in the CK OO metric suite [9], have been used to evaluate and predict fault-proneness [3, 8].”
Software development cost estimation approaches — A survey (2000, positive): “POPs is a new way of sizing object oriented development projects and was introduced in [Minkiewicz 1998] based on previous work one in Object Oriented (OO) metrics done by Chidamber et al. and others [Chidamber and Kemerer 1994; Henderson-Sellers 1996].”
The influence of organizational structure on software quality (2008, neutral): “From the O-O (object-oriented) perspective the CK metric suite [7] consist of six metrics (designed primarily as object oriented design measures): weighted methods per class (WMC), coupling between objects (CBO), depth of inheritance (DIT), number of children (NOC), response for a class (RFC) and…”
Identification of Move Method Refactoring Opportunities (2009, neutral): “Other more coarse-grained metrics, such as CBO [13] and Coupling Factor [10], have not been considered because they estimate coupling based on the number of coupled classes and, therefore, their value might not change when a method is moved.”
Easy over hard: a case study on deep learning (2017, neutral): “However, no further information regarding runtime as well as experimental hardware is reported for both methods.”
Dealing with noise in defect prediction (2011, neutral): “• Object-oriented metrics: including the WMC, CBO, NOC, DIT, LCOM, RFC metrics that are proposed by Chidamber and Kemerer [6].”
Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes (2007, positive): “El Emam et al. give a summary in [7] of 17 prior research studies undertaken to empirically validate the usefulness of OO metrics in identifying fault-prone classes; these include [6], [8], [9], [3], [10].”
Mining metrics to predict component failures (2006, positive): “The object-oriented metrics used in our approach were initially suggested by Chidamber and Kemerer [8].”
A systematic review of machine learning techniques for software fault prediction (2015, positive): “A number of OO metrics have also been used for SFP and the used metrics suite was defined by Chidamber and Kemerer [13] which was 28% studies which use OO metrics.”
The evolution of sentiment analysis - A review of research topics, venues, and top cited papers (2016, neutral): “us work. Top-cited software engineering papers from [16] Sentiment analysis and opinion mining Rank Title Year Citations Title and citation Year Citations 1 A metrics suite for object-oriented design [46] 1994 1,817 Opinion mining and sentiment analysis [27] 2008 2,487 2 QoS - aware middleware for W”