@Article{BarbosaSoSaSiBaVi:2022:SyLiRe,
               author = "Barbosa, Gerson and Souza, {\'E}rica Ferreira de and Santos, 
                         Luciana Brasil Rebelo dos and Silva, Marlon da and Balera, Juliana 
                         Marino and Vijaykumar, Nandamudi Lankalapalli",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Universidade 
                         Tecnol{\'o}gica Federal do Paran{\'a} (UTFPR)} and Instituto 
                         Federal de Educa{\c{c}}{\~a}o, Ci{\^e}ncia e Tecnologia de 
                         S{\~a}o Paulo (IFSP) and Instituto Federal de 
                         Educa{\c{c}}{\~a}o, Ci{\^e}ncia e Tecnologia de S{\~a}o Paulo 
                         (IFSP) and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "A Systematic Literature Review on prioritizing software test cases 
                         using Markov chains",
              journal = "Information and Software Technology",
                 year = "2022",
               volume = "147",
                pages = "e106902",
                month = "July",
             keywords = "Markov Chains, Systematic Literature Review, Test case 
                         prioritization.",
             abstract = "Context: Software Testing is a costly activity since the size of 
                         the test case set tends to increase as the construction of the 
                         software evolves. Test Case Prioritization (TCP) can reduce the 
                         effort and cost of software testing. TCP is an activity where a 
                         subset of the existing test cases is selected in order to maximize 
                         the possibility of finding defects. On the other hand, Markov 
                         Chains representing a reactive system, when solved, can present 
                         the occupation time of each of their states. The idea is to use 
                         such information and associate priority to those test cases that 
                         consist of states with the highest probabilities. Objective: The 
                         objective of this paper is to conduct a survey to identify and 
                         understand key initiatives for using Markov Chains in TCP. Aspects 
                         such as approaches, developed techniques, programming languages, 
                         analytical and simulation results, and validation tests are 
                         investigated. Methods: A Systematic Literature Review (SLR) was 
                         conducted considering studies published up to July 2021 from five 
                         different databases to answer the three research questions. 
                         Results: From SLR, we identified 480 studies addressing Markov 
                         Chains in TCP that have been reviewed in order to extract relevant 
                         information on a set of research questions. Conclusion: The final 
                         12 studies analyzed use Markov Chains at some stage of test case 
                         prioritization in a distinct way, that is, we found that there is 
                         no strong relationship between any of the studies, not only on how 
                         the technique was used but also in the context of the application. 
                         Concerning the fields of application of this subject, 6 forms of 
                         approach were found: Controlled Markov Chain, Usage Model, 
                         Model-Based Test, Regression Test, Statistical Test, and Random 
                         Test. This demonstrates the versatility and robustness of the 
                         tool. A large part of the studies developed some prioritization 
                         tool, being its validation done in some cases analytically and in 
                         others numerically, such as: Measure of the software 
                         specification, Optimal Test Transition Probabilities, Adaptive 
                         Software Testing, Automatic Prioritization, Ant Colony 
                         Optimization, Model Driven approach, and Monte Carlo Random 
                         Testing.",
                  doi = "10.1016/j.infsof.2022.106902",
                  url = "http://dx.doi.org/10.1016/j.infsof.2022.106902",
                 issn = "0950-5849",
             language = "en",
           targetfile = "Barbosa_2022.pdf",
        urlaccessdate = "20 maio 2024"
}