This paper introduces the Java Software Evolution Tracker, a visualization and analysis tool that provides practitioners the means to examine the evolution of a software system from a top to bottom perspective, starting with changes in the graphical user interface all the way to source code modifications.
For the interested reader, a detailed evaluation concerning software visualizers that takes into account effectiveness and presentation techniques is avalable in @cite_3 .
@cite_3
1992743299 2147787175 2108958711 2009678853
We present an assessment of the state and historic development of evaluation practices as reported in papers published at the IEEE Visualization conference. Our goal is to reflect on a meta-level about evaluation in our community through a systematic understanding of the characteristics and goals of presented evaluations. For this purpose we conducted a systematic review of ten years of evaluations in the published papers using and extending a coding scheme previously established by [2012]. The results of our review include an overview of the most common evaluation goals in the community, how they evolved over time, and how they contrast or align to those of the IEEE Information Visualization conference. In particular, we found that evaluations specific to assessing resulting images and algorithm performance are the most prevalent (with consistently 80-90 of all papers since 1997). However, especially over the last six years there is a steady increase in evaluation methods that include participants, either by evaluating their performances and subjective feedback or by evaluating their work practices and their improved analysis and reasoning capabilities using visual tools. Up to 2010, this trend in the IEEE Visualization conference was much more pronounced than in the IEEE Information Visualization conference which only showed an increasing percentage of evaluation through user performance and experience testing. Since 2011, however, also papers in IEEE Information Visualization show such an increase of evaluations of work practices and analysis as well as reasoning using visual tools. Further, we found that generally the studies reporting requirements analyses and domain-specific work practices are too informally reported which hinders cross-comparison and lowers external validity. Many software visualization (SoftVis) tools are continuously being developed by both researchers as well as software development companies. In order to determine if the developed tools are effective in helping their target users, it is desirable that they are exposed to a proper evaluation. Despite this, there is still lack of a general guideline on how these evaluations should be carried out and many of the tool developers perform very limited or no evaluation of their tools. Each person that carries out one evaluation, however, has experiences which, if shared, can guide future evaluators. This paper presents the lessons learned from evaluating over 20 SoftVis tools with over 90 users in five different studies spread on a period of over two years. The lessons covered include the selection of the tools, tasks, as well as evaluation participants. Other discussed points are related to the duration of the evaluation experiment, its location, the procedure followed when carrying out the experiment, as well as motivation of the participants. Finally, an analysis of the lessons learned is shown with the hope that these lessons will be of some assistance to future SoftVis tool evaluators. Finding and fixing performance bottlenecks requires sound knowledge of the program that is to be optimized. In this paper, we propose an approach for presenting performance-related information to software engineers by visually augmenting source code shown in an editor. Small diagrams at each method declaration and method call visualize the propagation of runtime consumption through the program as well as the interplay of threads in parallelized programs. Advantages of in situ visualization like this over traditional representations, where code and profiling information are shown in different places, promise to be the prevention of a split-attention effect caused by multiple views; information is presented where required, which supports understanding and navigation. We implemented the approach as an IDE plug-in and tested it in a user study with four developers improving the performance of their own programs. The user study provides insights into the process of understanding performance bottlenecks with our approach. Effective visual features are essential for computational aesthetic quality rating systems. Existing methods used machine learning and statistical modeling techniques on handcrafted features or generic image descriptors. A recently-published large-scale dataset, the AVA dataset, has further empowered machine learning based approaches. We present the RAPID (RAting PIctorial aesthetics using Deep learning) system, which adopts a novel deep neural network approach to enable automatic feature learning. The central idea is to incorporate heterogeneous inputs generated from the image, which include a global view and a local view, and to unify the feature learning and classifier training using a double-column deep convolutional neural network. In addition, we utilize the style attributes of images to help improve the aesthetic quality categorization accuracy. Experimental results show that our approach significantly outperforms the state of the art on the AVA dataset.