@book{DuerschReinMattisetal.2022,
  author    = {D{\"u}rsch, Falco and Rein, Patrick and Mattis, Toni and Hirschfeld, Robert},
  title     = {Learning from failure},
  number    = {145},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-528-6},
  issn      = {1613-5652},
  doi       = {10.25932/publishup-53755},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-537554},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {87},
  year      = {2022},
  abstract  = {Regression testing is a widespread practice in today's software industry to ensure software product quality. Developers derive a set of test cases, and execute them frequently to ensure that their change did not adversely affect existing functionality. As the software product and its test suite grow, the time to feedback during regression test sessions increases, and impedes programmer productivity: developers wait longer for tests to complete, and delays in fault detection render fault removal increasingly difficult. Test case prioritization addresses the problem of long feedback loops by reordering test cases, such that test cases of high failure probability run first, and test case failures become actionable early in the testing process. We ask, given test execution schedules reconstructed from publicly available data, to which extent can their fault detection efficiency improved, and which technique yields the most efficient test schedules with respect to APFD? To this end, we recover regression 6200 test sessions from the build log files of Travis CI, a popular continuous integration service, and gather 62000 accompanying changelists. We evaluate the efficiency of current test schedules, and examine the prioritization results of state-of-the-art lightweight, history-based heuristics. We propose and evaluate a novel set of prioritization algorithms, which connect software changes and test failures in a matrix-like data structure. Our studies indicate that the optimization potential is substantial, because the existing test plans score only 30\% APFD. The predictive power of past test failures proves to be outstanding: simple heuristics, such as repeating tests with failures in recent sessions, result in efficiency scores of 95\% APFD. The best-performing matrix-based heuristic achieves a similar score of 92.5\% APFD. In contrast to prior approaches, we argue that matrix-based techniques are useful beyond the scope of effective prioritization, and enable a number of use cases involving software maintenance. We validate our findings from continuous integration processes by extending a continuous testing tool within development environments with means of test prioritization, and pose further research questions. We think that our findings are suited to propel adoption of (continuous) testing practices, and that programmers' toolboxes should contain test prioritization as an existential productivity tool.},
  language  = {en}
}
@book{WeyandChromikWolfetal.2017,
  author    = {Weyand, Christopher and Chromik, Jonas and Wolf, Lennard and K{\"o}tte, Steffen and Haase, Konstantin and Felgentreff, Tim and Lincke, Jens and Hirschfeld, Robert},
  title     = {Improving hosted continuous integration services},
  number    = {108},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-377-0},
  issn      = {1613-5652},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-94251},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {viii, 114},
  year      = {2017},
  abstract  = {Developing large software projects is a complicated task and can be demanding for developers. Continuous integration is common practice for reducing complexity. By integrating and testing changes often, changesets are kept small and therefore easily comprehensible. Travis CI is a service that offers continuous integration and continuous deployment in the cloud. Software projects are build, tested, and deployed using the Travis CI infrastructure without interrupting the development process. This report describes how Travis CI works, presents how time-driven, periodic building is implemented as well as how CI data visualization can be done, and proposes a way of dealing with dependency problems.},
  language  = {en}
}