Visualizing Co-Change Information with the Evolution Radar

D’Ambros, Marco; Lanza, Michele; Lungu, Mircea

doi:10.1109/tse.2009.17

Cited by 68 publications

(52 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Ambros et al [18] presented the Evolution Radar, an approach to integrate and visualise module-level and filelevel logical couplings, which is useful to answer questions about the evolution of a program; the impact of changes at different levels of abstraction and the need for restructuring. Beyer and Hassan [26] introduced the evolution storyboard, a new concept for animated visualisations of historical information about the program structure, and the storyboard panel, which highlights structural differences between two versions of a program.…”

Section: Change Propagationmentioning

confidence: 99%

An Exploratory Study of Macro Co-changes

Jaafar

Guéhéneuc

Hamel

et al. 2011

2011 18th Working Conference on Reverse Engineering

View full text Add to dashboard Cite

Abstract-The literature describes several approaches to identify the artefacts of programs that change together to reveal the (hidden) dependencies among these artefacts. These approaches analyse historical data, mined from version control systems, and report co-changing artefacts, which hint at the causes, consequences, and actors of the changes. We introduce the novel concepts of macro co-changes (MCC), i.e., of artefacts that co-change within a large time interval, and of dephase macro co-changes (DMCC), i.e., macro co-changes that always happen with the same shifts in time. We describe typical scenarios of MCC and DMCC and we use the Hamming distance to detect approximate occurrences of MCC and DMCC. We present our approach, Macocha, to identify these concepts in large programs. We apply Macocha and compare it in terms of precision and recall with UMLDiff (file stability) and association rules (co-changing files) on four systems: ArgoUML, FreeBSD, SIP, and XalanC. We also use external information to validate the (approximate) MCC and DMCC found by Macocha. We thus answer two research questions showing the existence and usefulness of theses concepts and explaining scenarios of hidden dependencies among artefacts.

show abstract

Section: Change Propagationmentioning

confidence: 99%

An Exploratory Study of Macro Co-changes

Jaafar

Guéhéneuc

Hamel

et al. 2011

2011 18th Working Conference on Reverse Engineering

View full text Add to dashboard Cite

show abstract

“…The Visualization module supports software evolution analysis by creating and exporting interactive Scalable Vector Graphics (SVG) visualizations. The visualizations are created by two external tools: Mondrian [42] and the Evolution Radar [10]. The visualization module converts these visualization to SVG graphics.…”

Section: Architecturementioning

confidence: 99%

“…For example, Churrasco reuses two external tools (Mondrian [42] and the Evolution Radar [10]) to create visualizations, which are then converted to SVG by a dedicated module of Churrasco (see Fig. 9a).…”

Section: Peril 6 -Interactionmentioning

confidence: 99%

“…The Ownership Map [23], introduced by Gîrba et al visualizes code ownership of files over time, based on information extracted from CVS logs. The Evolution Radar visualizes co-change information extracted from SCM logs, integrating different levels of abstraction, to support the analysis of the coupling at the module level and the understanding of the causes at the file level [10].…”

Section: Design-level Visualizationmentioning

confidence: 99%

“…In the past years, we have developed a number of software visualizations tools, such as CodeCrawler [31], Softwarenaut [36], BugCrawler [8], Evolution Radar [10], Bug's Life [11], CodeCity [60], Churrasco [7], The Small Project Observatory [38], and Spyware [48]. Many of these tools are available, but some effort from accidental users to make them work is required, decreasing their adoption and impact.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On porting software visualization tools to the web

D’Ambros

Lanza

Lungu

et al. 2010

Int J Softw Tools Technol Transfer

View full text Add to dashboard Cite

Detecting asynchrony and dephase change patterns by mining software repositories

Jaafar

Guéhéneuc

Hamel

et al. 2013

J Software Evolu Process

View full text Add to dashboard Cite

SUMMARYSoftware maintenance accounts for the largest part of the costs of any program. During maintenance activities, developers implement changes (sometimes simultaneously) on artifacts in order to fix bugs and to implement new requirements. To reduce this part of the costs, previous work proposed approaches to identify the artifacts of programs that change together. These approaches analyze historical data, mined from version control systems, and report change patterns, which lead at the causes, consequences, and actors of the changes to source code files. They also introduce so-called change patterns that describe some typical change dependencies among files. In this paper, we introduce two novel change patterns: the asynchrony change pattern, corresponding to macro co-changes (MC), that is, of files that co-change within a large time interval (change periods) and the dephase change pattern, corresponding to dephase macro co-changes (DC), that is, MC that always happens with the same shifts in time. We present our approach, that we named Macocha, to identify these two change patterns in large programs. We use the k-nearest neighbor algorithm to group changes into change periods. We also use the Hamming distance to detect approximate occurrences of MC and DC. We apply Macocha and compare its performance in terms of precision and recall with UMLDiff (file stability) and association rules (co-changing files) on seven systems: ArgoUML, FreeBSD, JFreeChart, Openser, SIP, XalanC, and XercesC developed with three different languages (C, C++, and Java). These systems have a size ranging from 532 to 1693 files, and during the study period, they have undergone 1555 to 23,944 change commits. We use external information and static analysis to validate (approximate) MC and DC found by Macocha. Through our case study, we show the existence and usefulness of these novel change patterns to ease software maintenance and, potentially, reduce related costs.

show abstract

Visualizing Co-Change Information with the Evolution Radar

Cited by 68 publications

References 27 publications

An Exploratory Study of Macro Co-changes

An Exploratory Study of Macro Co-changes

On porting software visualization tools to the web

Detecting asynchrony and dephase change patterns by mining software repositories

Contact Info

Product

Resources

About