Visualizing the Bug Distribution Information Available in Software Bug Repositories

Visualizing the Bug Distribution Information Available in Software Bug Repositories

N. K. Nagwani (National Institute of Technology, India) and S. Verma (National Institute of Technology, India)
DOI: 10.4018/978-1-5225-1837-2.ch058
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Abstract

Software repositories contain a wealth of information that can be analyzed for knowledge extraction. Software bug repositories are one such repository that stores the information about the defects identified during the development of software. Information available in software bug repositories like number of bugs priority-wise, component-wise, status-wise, developers-wise, module-wise, summary-terms-wise, can be visualized with the help of two- or three-dimensional graphs. These visualizations help in understanding the bug distribution patterns, software matrices related to the software bugs, and developer information in the bug-fixing process. Visualization techniques are exploited with the help of open source technologies in this chapter to visualize the bug distribution information available in the software bug repositories. Two-dimensional and three-dimensional graphs are generated using java-based open source APIs, namely Jzy3d (Java Easy 3d) and JFreeChart. Android software bug repository is selected for the experimental demonstrations of graphs. The textual bug attribute information is also visualized using frequencies of frequent terms present in it.
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There is a magic in graphs. The profile of a curve reveals in a flash a whole situation — the life history of an epidemic, a panic, or an era of prosperity. The curve informs the mind, awakens the imagination, convinces.- Henry D. Hubbard in Brinton

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Visual Analytics And Visual Data Mining

Information is increasing drastically and is a well-known observable fact of the information age. Computing techniques are also becoming advanced day by day to collect and store the data. However, performing data analysis on this huge amount data is a critical problem and is the need of the day. It is one of the primary activity of knowledge workers is to extract the knowledge and useful patterns from the vast amount of data. A number of software tools exist for this purpose using which knowledge can be retrieved from the high volume of data. Although the numbers of software tools exist, still there is a major challenge of performing data analysis in handling the high volume of data. To bridge this gap visual analytics is introduced as one of the smart and quick way of analysis over the huge amount of data. Visual analytics uses graphs, charts and other visual objects to represent the information which allows an intelligent mechanism for direct interaction of users to perform data analysis and make fruitful conclusions in faster manners. The basic concept of visual analytics is information present in one graph is equivalent to information present in number of files (Keim et al., 2006).

In general, visual analytics can be described as “the science of analytical reasoning facilitated by interactive visual interfaces”. Visual analytics is consisting of a number of steps, some of the common steps in visual analytics are information collection, preprocessing of data, knowledge representation, user interaction, and decision making. Visual analytics is more than just visualization and can rather be seen as an integrated approach combining visualization, human factors and data analysis. Visual analytics consumes the techniques from knowledge discovery in databases (KDD), statistics and mathematics to derive the data analysis and uses various visualization capabilities to represent the conclusions, results and outcome of data analysis techniques in terms of user understandable visual objects like graph, charts etc. Visualization and visual analysis play important roles in discovering, analyzing and presenting vast amount of data. A survey of applying the visualization techniques over multi-faceted data like spatio-temporal and multi-variate are discussed by (Kehrer et al., 2013).

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