Embedding Spatial Software Visualization in the IDE: an Exploratory Study

Software visualization can be of great use for understanding and exploring a software system in an intuitive manner. Spatial representation of software is a promising approach of increasing interest. However, little is known about how developers interact with spatial visualizations that are embedded in the IDE. In this paper, we present a pilot study that explores the use of Software Cartography for program comprehension of an unknown system. We investigated whether developers establish a spatial memory of the system, whether clustering by topic offers a sound base layout, and how developers interact with maps. We report our results in the form of observations, hypotheses, and implications. Key findings are a) that developers made good use of the map to inspect search results and call graphs, and b) that developers found the base layout surprising and often confusing. We conclude with concrete advice for the design of embedded software maps.Comment: To appear in proceedings of SOFTVIS 2010 conferenc

AskTheCode: Interactive Call Graph Exploration for Error Fixing and Prevention

In order to prevent and fix errors in program code, developers need to understand its semantics to a significant extent. For this purpose, they use various approaches, such as manual call graph exploration or dynamic analysis with a debugger. However, these techniques tend to be cumbersome in a larger codebase, because they provide either underapproximate or overapproximate results and it is often hard to combine them. Therefore, we present AskTheCode, a Microsoft Visual Studio extension enabling to interactively explore a call graph, ensuring that only feasible execution traces are taken into consideration. AskTheCode is based on control flow analysis and backward symbolic execution. We show its potential to significantly improve developers' experience on a complex code example

Find Unique Usages: Helping Developers Understand Common Usages

When working in large and complex codebases, developers face challenges using \textit{Find Usages} to understand how to reuse classes and methods. To better understand these challenges, we conducted a small exploratory study with 4 participants. We found that developers often wasted time reading long lists of similar usages or prematurely focused on a single usage. Based on these findings, we hypothesized that clustering usages by the similarity of their surrounding context might enable developers to more rapidly understand how to use a function. To explore this idea, we designed and implemented \textit{Find Unique Usages}, which extracts usages, computes a diff between pairs of usages, generates similarity scores, and uses these scores to form usage clusters. To evaluate this approach, we conducted a controlled experiment with 12 participants. We found that developers with Find Unique Usages were significantly faster, completing their task in 35% less time

Exploiting UML dynamic object modeling for the visualization of C++ programs

In this paper we present an approach to modeling and visualizing the dynamic interactions among objects in a C++ application. We exploit UML diagrams to expressively visualize both the static and dynamic properties of the application. We make use of a class diagram and call graph of the application to select the parts of the application to be modeled, thereby reducing the number of objects and methods under consideration with a concomitant reduction in the cognitive burden on the user of our system. We use aspects to insert probes into the application to enable profiling of the interactions of objects and methods and we visualize these interactions by providing sequence and communication diagrams for the parts of the program under consideration. We complement our static selectors with dynamic selectors that enable the user to further filter objects and methods from the sequence and communication diagrams, further enhancing the cognitive economy of our system. A key feature of our approach is the provision for dynamic interaction with both the profiler and the application. Interaction with the profiler enables filtering of methods and objects. Interaction with the application enables the user to supply input to the application to provide direction and enhance comprehension or debugging

Comprehending Variability in Analysis Results of Software Product Lines

Analyses of a software product line (SPL) typically report variable results that are annotated with logical expressions indicating the set of product variants for which the results hold. These expressions can get complicated and difficult to reason about when the SPL has lots of features and product variants. Previous work introduced a visualizer that supports filters for highlighting the analysis results that apply to product variants of interest, but this work was weakly evaluated. In this paper, we report on a controlled user study that evaluates the effectiveness of this new visualizer in helping the user search variable results and compare the results of multiple variants. Our findings indicate that the use of the new visualizer significantly improves the correctness and efficiency of the user's work and reduces the user's cognitive load in working with variable results

Augmenting bug localization with part-of-speech and invocation

Bug localization represents one of the most expensive, as well as time-consuming, activities during software maintenance and evolution. To alleviate the workload of developers, numerous methods have been proposed to automate this process and narrow down the scope of reviewing buggy files. In this paper, we present a novel buggy source-file localization approach, using the information from both the bug reports and the source files. We leverage the part-of-speech features of bug reports and the invocation relationship among source files. We also integrate an adaptive technique to further optimize the performance of the approach. The adaptive technique discriminates Top 1 and Top N recommendations for a given bug report and consists of two modules. One module is to maximize the accuracy of the first recommended file, and the other one aims at improving the accuracy of the fixed defect file list. We evaluate our approach on six large-scale open source projects, i.e. ASpectJ, Eclipse, SWT, Zxing, Birt and Tomcat. Compared to the previous work, empirical results show that our approach can improve the overall prediction performance in all of these cases. Particularly, in terms of the Top 1 recommendation accuracy, our approach achieves an enhancement from 22.73% to 39.86% for ASpectJ, from 24.36% to 30.76% for Eclipse, from 31.63% to 46.94% for SWT, from 40% to 55% for ZXing, from 7.97% to 21.99% for Birt, and from 33.37% to 38.90% for Tomcat