
Datasets for the paper: "SpotFlow: Tracking Method Calls and States at Runtime". Datasets generated with SpotFlow (https://github.com/andrehora/spotflow). We analyze the test suites of 15 Python libraries: gzip, calendar, locale, json, ast, csv, ftplib, collections, os, tarfile, pathlib, smtplib, argparse, configparser, and email. Dataset 1: Variables Values at Runtime. We extracted every variable name and their respective values at runtime. The dataset contains 1,234 distinct variables and a total of 133,169 distinct values. Dataset 2: Mapping Between Test Cases and Application Methods. We extracted every test method that executes at least one application method and mapped each test to their respective executed methods. The dataset contains 2,458 test methods and 42,218 executed application methods. In total, the application methods were executed 2,722,746 times by the tests.
| 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 |
