A study on the Application of Markov Chains to Prioritize Test Cases

The entire results and the inputs to run the experiment are available in this dataset. The data is composed of five folders one for each case study and one for the running example presented in Section 3. Within each folder, it is possible do see the input files, as well as the results, both for H1 and H2. The input formats can be consulted to understanding and potentially replication of the study. This dataset also contains the algorithm, in Python. The algorithm is available on GitHub, we did not put the link because of the blind review.

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

Found an issue? Give us feedback

0

Average