Test Case Prioritization Based on Data Reuse

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Details

Test Case Prioritization is an approach that aims to order test cases to obtain gains according to specific criteria. This work proposes test case prioritization techniques aiming to decrease the time spent in manual execution by reducing the effort of the data preparation needed for each test case; the better sequences of tests are those that reuse more data. We applied these techniques in a mobile phone testing environment where tests are manually executed and designed based on requirements.We propose a tool that mechanizes the prioritization process helping testers to register information, execute the prioritization techniques and choose from sequences yielded as results. Empirical studies were performed, comparing the permutation approach to the existing prioritization technique where the test cases are prioritized manually based on a heuristic that uses a tree structure and, knowledge and intuition from the testers. Results show gains of approximately 25-30% in configuration time. The techniques proposed yield significant results not just in the execution sequence but also in the sequence generation, which is automated by our tool.

Autorentext

Lucas Lima is a lecturer at Departamento de Estatística e Informática of Universidade Federal Rural de Pernambuco. He holds a Ph.D. from Universidade Federal de Pernambuco. His interests are in providing formal semantics for graphical modelling languages and developing formal veri cation methods for model-based designs using automated techniques.

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783844388213
    • Sprache Englisch
    • Größe H220mm x B150mm x T7mm
    • Jahr 2011
    • EAN 9783844388213
    • Format Kartonierter Einband
    • ISBN 3844388214
    • Veröffentlichung 15.06.2011
    • Titel Test Case Prioritization Based on Data Reuse
    • Autor Lucas Lima
    • Untertitel An approach to optimize productivity of test teams in black-box environments
    • Gewicht 191g
    • Herausgeber LAP LAMBERT Academic Publishing
    • Anzahl Seiten 116
    • Genre Informatik

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