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Quality metrics for software management and engineering

Quality metrics for software management and engineering

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In the last 10-15 years, a multitude of software quality metrics has been developed. The most well-known of these metrics are Halstead's Software Science measures, McCabe's cyclomatic number, Gilb's logical complexity metrics, Henry and Kafura's information flow metrics, and Yau and Collofello's stability measures. In particular, metrics appropriate for quantifying characteristics of software maintainability drew widespread attention from both managers and engineers, since the rising cost of maintaining software systems is still an important concern for software developers and customers: The resources invested in software maintenance have been estimated to consume two thirds of the life cycle costs of software. Quality metrics are considered an effective aid to help manage the software development and maintenance process. Their periodic application is viewed as a control system providing continuous feedback and allowing corrective actions during the whole development process. This paper surveys the state of the art in measuring and predicting the quality of software by means of metrics. Emphasis is placed on metrics which are suited to quantify maintainability characteristics in the different stages of software development. Results of empirical validation studies are reported to show the analytical and predictive power of these metrics. From the survey it is concluded how far current metrics can be used for software management and engineering.

Inspec keywords: software management; software quality; software maintenance

Other keywords: software developer; McCabes cyclomatic number; software quality metrics; Halstead Software Science measures; Gilbs logical complexity metrics; software maintainability; software management; Yau and Collofello's stability measures; Henry and Kafuras information flow metrics; software engineering; life cycle cost

Subjects: Software management; Software engineering techniques; Software metrics

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