A system for graph-based visualization of the evolution of software

Collberg, Christian; Kobourov, Stephen G.; Nagra, Jasvir; Pitts, Jacob; Wampler, Kevin

doi:10.1145/774833.774844

Cited by 122 publications

(61 citation statements)

References 17 publications

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“…When another change is affected, the vertex again becomes red. Note the number of changes between the two large clusters and the break in the build on the last image [24].…”

Section: Static Graph Layoutsmentioning

confidence: 99%

“…TGRIP could handle the larger graphs that appear in the real-world. It was developed as part of a system that keeps track of the evolution of software by extracting information about the program stored within a CVS version control system [24]. Such tools allow programmers to understand the evolution of a legacy program: Why is the program structured the way it is?…”

Section: Dynamic Graph Layoutsmentioning

confidence: 99%

“…Diehl and Görg [29] and Görg et al [49] consider graphs in a sequence to create smoother transitions. Animations as a means to convey an evolving underlying graph have also been used in the context of software evolution [24] and scientific literature visualization [35]. Creating smooth animation between changing sequences of graphs is addressed using spectral graph visualization in [15].…”

Section: Animation Versus Small Multiplesmentioning

confidence: 99%

“…When using the animation/morphing approach, it is possible to change the balance between readability of individual graphs and the overall mental map preservation, as in the system for Graph Animations with Evolving Layouts, GraphAEL [35,40]. Applications of this framework include visualizing software evolution [24], social networks analysis [9], and the behavior of dynamically modifiable code [30]. Robertson et al [89] evaluate the effectiveness of three trend visualization techniques.…”

Section: Animation Versus Small Multiplesmentioning

confidence: 99%

See 3 more Smart Citations

Temporal Multivariate Networks

Archambault¹,

Abello²,

Kennedy³

et al. 2014

Lecture Notes in Computer Science

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Abstract. Networks that evolve over time, or dynamic graphs, have been of interest to the areas of information visualization and graph drawing for many years. Typically, its the structure of the dynamic graph that evolves as vertices and edges are added or removed from the graph. In a multivariate scenario, however, attributes play an important role and can also evolve over time. In this chapter, we characterize and survey methods for visualizing temporal multivariate networks. We also explore future applications and directions for this emerging area in the fields of information visualization and graph drawing.

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“…When another change is affected, the vertex again becomes red. Note the number of changes between the two large clusters and the break in the build on the last image [24].…”

Section: Static Graph Layoutsmentioning

confidence: 99%

Section: Dynamic Graph Layoutsmentioning

confidence: 99%

Section: Animation Versus Small Multiplesmentioning

confidence: 99%

Section: Animation Versus Small Multiplesmentioning

confidence: 99%

See 2 more Smart Citations

Temporal Multivariate Networks

Archambault¹,

Abello²,

Kennedy³

et al. 2014

Lecture Notes in Computer Science

Self Cite

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“…Using the SEESOFT tool [2] the changes of software artifacts over time can be examined. Gevol [5] helps to watch structural changes.…”

Section: Related Work and Outlookmentioning

confidence: 99%

Visual Data Mining in Software Archives to Detect How Developers Work Together

Weißgerber

Pohl

Burch

2007

Fourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007)

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Analyzing the check-in information of open source software projects which use a version control system such as CVS or SUBVERSION can yield interesting and important insights into the programming behavior of developers. As in every major project tasks are assigned to many developers, the development must be coordinated between these programmers. This paper describes three visualization techniques that help to examine how programmers work together, e.g. if they work as a team or if they develop their part of the software separate from each other. Furthermore, phases of stagnation in the lifetime of a project can be uncovered and thus, possible problems are revealed.To demonstrate the usefulness of these visualization techniques we performed case studies on two open source projects. In these studies interesting patterns of developers' behavior, e.g. the specialization on a certain module can be observed. Moreover, modules that have been changed by many developers can be identified as well as such ones that have been altered by only one programmer.

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Understanding systematic and collaborative code changes by mining evolutionary trajectory patterns

Jiang

Peng

Wang

et al. 2017

J Software Evolu Process

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The life cycle of a large-scale software system can undergo many releases. Each release often involves hundreds or thousands of revisions committed by many developers over time. Many code changes are made in a systematic and collaborative way. However, such systematic and collaborative code changes are often undocumented and hidden in the evolution history of a software system. It is desirable to recover commonalities and associations among dispersed code changes in the evolutionary trajectory of a software system. In this paper, we present Summarizing Evolutionary Trajectory by Grouping and Aggregation (SETGA), an approach to summarizing historical commit records as trajectory patterns by grouping and aggregating relevant code changes committed over time. The SETGA extracts change operations from a series of commit records from version control systems. It then groups extracted change operations by their common properties from different dimensions such as change operation types, developers, and change locations.After that, SETGA aggregates relevant change operation groups by mining various associations among them. We implement SETGA and conduct an empirical study with 3 open-source systems.We investigate underlying evolution rules and problems that can be revealed by the identified patterns and analyze the evolution of trajectory patterns in different periods. The results show that SETGA can identify various types of trajectory patterns that are useful for software evolution management and quality assurance.

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A system for graph-based visualization of the evolution of software

Cited by 122 publications

References 17 publications

Temporal Multivariate Networks

Temporal Multivariate Networks

Visual Data Mining in Software Archives to Detect How Developers Work Together

Understanding systematic and collaborative code changes by mining evolutionary trajectory patterns

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