- Visual paradigm 11 download manual#
- Visual paradigm 11 download software#
- Visual paradigm 11 download code#
Visual paradigm 11 download software#
The dataset used in this paper was collected from trusted and accredited resources in Software Engineering field. In this work, we deeply investigate the relations among object-oriented metrics' parameters using concepts inspired from complex networks. In this case, understanding the relations among parameters may help developers when selecting metrics during the software design phase, which makes the assessment process accurate, wiser, and reduce time consumption. This means a parameter can be associated with several metrics. Many of the object-oriented metrics have parameters in common. Hence, each metric consists of one or several parameters. To calculate the value of a metric, it is needed to have its parameters involved.
Therefore, it is important to understand software metrics in a way that makes it wiser to select a particular metric in the software design assessment process. The quality of software design can be measured using these metrics. The use of software metrics has become a crucial tool during the software design phase.
Visual paradigm 11 download code#
Finally, the results indicate that FineCodeAnalyzer allows effectively locating the code elements than the developer’s adopted strategies.
Visual paradigm 11 download manual#
For cognitive-load, the developers found FineCodeAnalyzer to be 72% less complicated than manual strategies, in terms of the NASA Tool Load Index metric. Additionally, FineCodeAnalyzer takes 5% less time than developers’ strategies in terms of minutes of time. Specifically, FineCodeAnalyzer outperforms the developers’ strategies up to 47%, 76%, and 61% in terms of Precision, Recall, and F1-measure, respectively. For usefulness concern, the results show that FineCodeAnalyzer outperforms the developers’ self-adopted strategies in locating the code elements in terms of Precision, Recall, and F1-Measure of accurately locating the code elements. To evaluate the performance of FineCodeAnalyzer, we consider 74 developers that assess three main facets: (i) usefulness, (ii) cognitive-load, and (iii) time efficiency. This work proposes a tool (called as FineCodeAnalyzer) that supports an interactive source code analysis grounded on structural and historical relations at fine granular-level between the source code elements. Moreover, existing solutions seem less useful for the developers. However, the majority of the reported techniques are limited to textual analysis where the real developers’ concerns are not properly considered. Thus, the developers seek automated support in performing the software maintenance tasks through automated tools and techniques.
finding the location of buggy code from a large application) is an expensive, time-consuming, tedious, and challenging task. However, handling the aforementioned tasks on a manual basis (i.e. Source code analysis is one of the important activities during the software maintenance phase that focuses on performing the tasks including bug localization, feature location, bug/feature assignment, and so on.
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