A STUDY OF SEMI-AUTOMATED TRACING
2011
- 504Usage
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
- Usage504
- Downloads356
- Abstract Views148
Thesis / Dissertation Description
Requirements tracing is crucial for software engineering practices including change analysis, regression testing, and reverse engineering. The requirements tracing process produces a requirements traceability matrix(TM) which links high- and low-level document elements. Manually generating a TM is laborious, time consuming, and error-prone. Due to these challenges TMs are often neglected. Automated information retrieval(IR) techniques are used with some efficiency. However, in mission- or safety-critical systems a human analyst is required to vet the candidate TM. This introduces semi-automated requirements tracing, where IR methods present a candidate TM and a human analyst validates it, producing a final TM. In semi-automated tracing the focus becomes the quality of the final TM. This thesis expands upon the research of Cuddeback et al. by examining how human analysts interact with candidate TMs. We conduct two experiments, one using an automated tracing tool and the other using manual validation. We conduct formal statistical analysis to determine the key factors impacting the analyst’s tracing performance. Additionally, we conduct a pilot study investigating how analysts interact with TMs generated by automated IR methods. Our research statistically confirms the finding of Cuddeback et al. that the strongest impact on analyst performance is the initial TM quality. Finally we show evidence that applying local filters to IR results produce the best candidate TMs.
Bibliographic Details
http://digitalcommons.calpoly.edu/theses/574; http://dx.doi.org/10.15368/theses.2011.130; https://digitalcommons.calpoly.edu/theses/574; https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=1604&context=theses; https://dx.doi.org/10.15368/theses.2011.130; https://digitalcommons.calpoly.edu/theses/574/
Robert E. Kennedy Library, Cal Poly
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know