Advantages using sloc software metric examples

A red rating is a rating between 0 and 9 and indicates low maintainability. A yellow rating is between 10 and 19 and indicates that the code is moderately maintainable. A green rating is between 20 and 100 and indicates that the code has good maintainability. Color coded ratings can be used to quickly identify trouble spots in your code.

A high value means better maintainability. Maintainability Index - Calculates an index value between 0 and 100 that represents the relative ease of maintaining the code. The following list shows the code metrics results that Visual Studio calculates: Code metrics data can be generated for an entire solution or a single project.įor information about how to generate code metrics data in Visual Studio, see How to: Generate code metrics data. Development teams can identify potential risks, understand the current state of a project, and track progress during software development.ĭevelopers can use Visual Studio to generate code metrics data that measure the complexity and maintainability of their managed code. By taking advantage of code metrics, developers can understand which types and/or methods should be reworked or more thoroughly tested. Code metrics is a set of software measures that provide developers better insight into the code they are developing. The increased complexity of modern software applications also increases the difficulty of making the code reliable and maintainable.

Copyright © 2012 John Wiley & Sons, Ltd.Applies to: Visual Studio Visual Studio for Mac Visual Studio Code Additionally, we compare the Squale model to both traditional aggregation techniques (e.g., the arithmetic mean), and to econometric inequality indices (e.g., the Gini or the Theil indices), recently applied to aggregation of software metrics. We empirically validate the adequacy of Squale through experiments on Eclipse. We then present a solution through the Squale model for metric aggregation, a model specifically designed to address the needs of practitioners. In this paper we derive, from our experience on real industrial cases and from the scientific literature, requirements for an aggregation method. Second, because a software evaluation requires the use of different metrics, with possibly widely varying output ranges, there is a need to combine these results into a unified quality assessment. First, because most software quality metrics are defined at the level of individual software components, there is a need for aggregation methods to summarize the results at the system level. With the growing need for quality assessment of entire software systems in the industry, new issues are emerging.

Advantages using sloc software metric examples