Measuring the quality of structured designs

Troy, Douglas; Zweben, Stuart H.

doi:10.1016/0164-1212(81)90031-5

Cited by 95 publications

(40 citation statements)

References 5 publications

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“…[15,39]. In this area, the quality of a design is often related to five design dimensions: (i) coupling, (ii) cohesion, (iii) complexity, (iv) modularity, and (v) size [10,35,36]. A number of studies demonstrate the significant correlation of software quality metrics with errors in the software design (e.g.…”

Section: Related Workmentioning

confidence: 99%

On a Quest for Good Process Models: The Cross-Connectivity Metric

Vanderfeesten

Reijers

Mendling

et al. 2008

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

100

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Abstract. Business process modeling is an important corporate activity, but the understanding of what constitutes good process models is still rather limited. In this paper, we turn to the cognitive dimensions framework and identify the understanding of the structural relationship between any pair of model elements as a hard mental operation. Based on the weakest-link metaphor, we introduce the cross-connectivity metric that measures the strength of the links between process model elements. The definition of this new metric builds on the hypothesis that process models are easier understood and contain less errors if they have a high cross-connectivity. We undertake a thorough empirical evaluation to test this hypothesis and present the findings. The good performance of this novel metric underlines the importance of cognitive research for advancing the field of process model measurement.

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Section: Related Workmentioning

confidence: 99%

On a Quest for Good Process Models: The Cross-Connectivity Metric

Vanderfeesten

Reijers

Mendling

et al. 2008

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

100

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“…Therefore, the stronger the coupling between modules, i.e., the more inter-related they are, the more difficult these modules are to understand, change, and correct and thus the more complex the resulting software system. Some empirical evidence exists to support this theory for structured development techniques; see, e.g., [33], [36].…”

Section: Introductionmentioning

confidence: 99%

A unified framework for coupling measurement in object-oriented systems

Briand

Daly

Wüst

1999

IIEEE Trans. Software Eng.

614

385

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Abstract-The increasing importance being placed on software measurement has led to an increased amount of research developing new software measures. Given the importance of object-oriented development techniques, one specific area where this has occurred is coupling measurement in object-oriented systems. However, despite a very interesting and rich body of work, there is little understanding of the motivation and empirical hypotheses behind many of these new measures. It is often difficult to determine how such measures relate to one another and for which application they can be used. As a consequence, it is very difficult for practitioners and researchers to obtain a clear picture of the state-of-the-art in order to select or define measures for object-oriented systems.This situation is addressed and clarified through several different activities. First, a standardized terminology and formalism for expressing measures is provided which ensures that all measures using it are expressed in a fully consistent and operational manner. Second, to provide a structured synthesis, a review of the existing frameworks and measures for coupling measurement in object-oriented systems takes place. Third, a unified framework, based on the issues discovered in the review, is provided and all existing measures are then classified according to this framework.This paper contributes to an increased understanding of the state-of-the-art: A mechanism is provided for comparing measures and their potential use, integrating existing measures which examine the same concepts in different ways, and facilitating more rigorous decision making regarding the definition of new measures and the selection of existing measures for a specific goal of measurement. In addition, our review of the state-of-the-art highlights that many measures are not defined in a fully operational form, and relatively few of them are based on explicit empirical models, as recommended by measurement theory.

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“…Coupling between software modules strengthens the dependency of one module on others and increases the probability that changes in one module may affect the other modules, which makes maintenance and evolution difficult and more likely to introduce regression faults (Banker et al, 1993;Schach et al, 2003;Yu et al, 2004). It has been shown that strong coupling is related to fault-proneness of a software system (Kafura and Henry, 1981; Selby and Basili, 1991; Troy and Zweben, 1981). Furthermore, the faultproneness of one module can adversely affect the maintenance and evolution of a number of other modules.…”

Section: Data Couplingmentioning

confidence: 99%

Examining the Relationships between Software Coupling and Software Performance: A Cross-platform Experiment

Ramaswamy²

2011

CIT

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Coupling measures the degree of dependencies between software modules. Considerable research has been performed to relate software coupling with software understandability, maintainability, and reusability, which are the key properties of software maintenance and evolution. However, only a few research works have been reported that study the relationships between software coupling and software performance. This study implemented two benchmarks that measure and compare the performance of software programs implemented with different kinds of coupling, common coupling, data coupling, and stamp coupling. The experiment is run on three different platforms, Windows, Linux, and Mac. The results show that (1) the relative performance of systems implemented using different software coupling is platform dependent; (2) while loose coupling is more favorable than strong coupling with respect to software maintenance and evolution, it has the drawback of reduced performance of a software program. Based on this study, we make some suggestions to balance the use of strong coupling and loose coupling in designing evolving software systems in order to achieve both maintainability and evolvability without compromising on performance.

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Measuring the quality of structured designs

Cited by 95 publications

References 5 publications

On a Quest for Good Process Models: The Cross-Connectivity Metric

On a Quest for Good Process Models: The Cross-Connectivity Metric

A unified framework for coupling measurement in object-oriented systems

Examining the Relationships between Software Coupling and Software Performance: A Cross-platform Experiment

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