@InProceedings{RebeloSBBSEVT:2023:PrIn,
               author = "Rebelo, Luciana and Souza, {\'E}rica and Berkenbrock, Gian and 
                         Barbosa, Gerson de Oliveira and Silva, Marlon and Endo, Andr{\'e} 
                         and Vijaykumar, Nandamudi Lankalapalli and Trubiani, Catia",
          affiliation = "Instituto Federal de Educa{\c{c}}{\~a}o, Ci{\^e}ncia e 
                         Tecnologia de S{\~a}o Paulo (IFSP) and {Universidade 
                         Tecnol{\'o}gica Federal do Paran{\'a} (UTFPR)} and {Universidade 
                         Federal de Santa Catarina (UFSC)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and Instituto Federal de 
                         Educa{\c{c}}{\~a}o, Ci{\^e}ncia e Tecnologia de S{\~a}o Paulo 
                         (IFSP) and {Universidade Federal de S{\~a}o Carlos (UFSCar)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Gran Sasso 
                         Science Institute}",
                title = "Prioritizing Test Cases with Markov Chains: A Preliminary 
                         Investigation",
            booktitle = "Proceedings...",
                 year = "2023",
               editor = "Bonfanti, S. and Gargantini, A. and Salvaneschi, P.",
                pages = "219--236",
         organization = "International Conference on Testing Software and Systems, 35.",
            publisher = "Springer",
                 note = "{Lecture Notes in Computer Science}",
             keywords = "Markov chain, Software Testing, Test Case Prioritization.",
             abstract = "Test Case Prioritization reduces the cost of software testing by 
                         executing earlier the subset of test cases showing higher 
                         priorities. The methodology consists of ranking test cases so 
                         that, in case of a limited budget, only the top-ranked tests are 
                         exercised. One possible direction for prioritizing test cases 
                         relies on considering the usage frequency of a software 
                         sub-system. To this end, a promising direction is to identify the 
                         likelihood of events occurring in software systems, and this can 
                         be achieved by adopting Markov chains. This paper presents a novel 
                         approach that analyzes the system scenarios modeled as a Markov 
                         chain and ranks the generated test sequences to prioritize test 
                         cases. To assess the proposed approach, we developed an algorithm 
                         and conducted a preliminary and experimental study that 
                         investigates the feasibility of using Markov chains as an 
                         appropriate means to prioritize test cases. We demonstrate the 
                         strength of the novel strategy by evaluating two heuristics, 
                         namely H1 (based on the transition probabilities) and H2 (based on 
                         the steady-state probabilities), with established metrics. Results 
                         show (i) coverage of 100% for both H1 and H2, and (ii) efficiency 
                         equal to 98.4% for H1 and 99.4% for H2, on average.",
  conference-location = "Bergamo",
      conference-year = "18-20 Sept. 2023",
                  doi = "10.1007/978-3-031-43240-8_14",
                  url = "http://dx.doi.org/10.1007/978-3-031-43240-8_14",
                 isbn = "978-303143239-2",
                 issn = "03029743",
             language = "en",
               volume = "14131",
        urlaccessdate = "29 jun. 2024"
}