Improving after-the-fact tracing and mapping: Supporting software quality predictions
IEEE Software, ISSN: 0740-7459, Vol: 22, Issue: 6, Page: 30-37
2005
- 39Citations
- 403Usage
- 46Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations39
- Citation Indexes39
- 39
- CrossRef24
- Usage403
- Downloads369
- Abstract Views34
- Captures46
- Readers46
- 46
Article Description
The requirements traceability matrix can successfully predict quality before code is written. However, its tedious development process, requiring analysts to manually discover and vet links between artifact levels, has stymied widespread adoption. The authors' tracing toolkit, called Retro (Requirements Tracing on Target), automates the information retrieval process, making RTM's development easier. Backed by empirical results, the authors describe Retro's advantages over other information retrieval approaches. © 2005 IEEE.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=28244483794&origin=inward; http://dx.doi.org/10.1109/ms.2005.156; http://ieeexplore.ieee.org/document/1524912/; http://xplorestaging.ieee.org/ielx5/52/32609/01524912.pdf?arnumber=1524912; https://digitalcommons.calpoly.edu/csse_fac/99; https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=1101&context=csse_fac
Institute of Electrical and Electronics Engineers (IEEE)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know