↓ Skip to main content

Critical evaluation of reverse engineering tool Imagix 4D!

Overview of attention for article published in SpringerPlus, December 2016
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
12 X users
facebook
2 Facebook pages
googleplus
1 Google+ user
reddit
1 Redditor

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
15 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 27%
Student > Ph. D. Student 3 20%
Student > Postgraduate 2 13%
Researcher 2 13%
Lecturer > Senior Lecturer 1 7%
Other 0 0%
Unknown 3 20%
Readers by discipline Count As %
Computer Science 3 20%
Engineering 3 20%
Psychology 2 13%
Social Sciences 2 13%
Neuroscience 1 7%
Other 0 0%
Unknown 4 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 10 October 2021.
All research outputs
#4,273,381
of 24,995,564 outputs
Outputs from SpringerPlus
#246
of 1,867 outputs
Outputs of similar age
#78,638
of 431,849 outputs
Outputs of similar age from SpringerPlus
#6
of 43 outputs
Altmetric has tracked 24,995,564 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,867 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. 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 431,849 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 81% of its contemporaries.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.