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Critical evaluation of reverse engineering tool Imagix 4D!

Overview of attention for article published in SpringerPlus, December 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

twitter
11 tweeters
facebook
2 Facebook pages
googleplus
1 Google+ user
reddit
1 Redditor

Citations

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1 Dimensions

Readers on

mendeley
11 Mendeley
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Title
Critical evaluation of reverse engineering tool Imagix 4D!
Published in
SpringerPlus, December 2016
DOI 10.1186/s40064-016-3732-x
Pubmed ID
Authors

Rashmi Yadav, Ravindra Patel, Abhay Kothari

Abstract

The comprehension of legacy codes is difficult to understand. Various commercial reengineering tools are available that have unique working styles, and are equipped with their inherent capabilities and shortcomings. The focus of the available tools is in visualizing static behavior not the dynamic one. Therefore, it is difficult for people who work in software product maintenance, code understanding reengineering/reverse engineering. Consequently, the need for a comprehensive reengineering/reverse engineering tool arises. We found the usage of Imagix 4D to be good as it generates the maximum pictorial representations in the form of flow charts, flow graphs, class diagrams, metrics and, to a partial extent, dynamic visualizations. We evaluated Imagix 4D with the help of a case study involving a few samples of source code. The behavior of the tool was analyzed on multiple small codes and a large code gcc C parser. Large code evaluation was performed to uncover dead code, unstructured code, and the effect of not including required files at preprocessing level. The utility of Imagix 4D to prepare decision density and complexity metrics for a large code was found to be useful in getting to know how much reengineering is required. At the outset, Imagix 4D offered limitations in dynamic visualizations, flow chart separation (large code) and parsing loops. The outcome of evaluation will eventually help in upgrading Imagix 4D and posed a need of full featured tools in the area of software reengineering/reverse engineering. It will also help the research community, especially those who are interested in the realm of software reengineering tool building.

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer > Senior Lecturer 2 18%
Student > Master 2 18%
Student > Ph. D. Student 1 9%
Student > Doctoral Student 1 9%
Researcher 1 9%
Other 1 9%
Unknown 3 27%
Readers by discipline Count As %
Computer Science 2 18%
Neuroscience 2 18%
Engineering 2 18%
Psychology 1 9%
Social Sciences 1 9%
Other 0 0%
Unknown 3 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 December 2016.
All research outputs
#2,757,504
of 14,537,474 outputs
Outputs from SpringerPlus
#237
of 1,773 outputs
Outputs of similar age
#91,056
of 376,995 outputs
Outputs of similar age from SpringerPlus
#76
of 456 outputs
Altmetric has tracked 14,537,474 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,773 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 376,995 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 456 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.