@Article{BaleraSant:2017:DeRiEv,
               author = "Balera, Juliana Marino and Santiago J{\'u}nior, Valdivino 
                         Alexandre de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "An algorithm for combinatorial interaction testing: definitions 
                         and rigorous evaluations",
              journal = "Journal of Software Engineering Research and Development",
                 year = "2017",
               volume = "5",
               number = "10",
             keywords = "oftware testing, Combinatorial interaction testing, Combinatorial 
                         testing, Mixed-value covering array, T-Tuple reallocation, 
                         Controlled experiment.",
             abstract = "Background: Combinatorial Interaction Testing (CIT) approaches 
                         have drawn attention of the software testing community to generate 
                         sets of smaller, efficient, and effective test cases where they 
                         have been successful in detecting faults due to the interaction of 
                         several input parameters. Recent empirical studies show that 
                         greedy algorithms are still competitive for CIT. It is thus 
                         interesting to investigate new approaches to address CIT test case 
                         generation via greedy solutions and to perform rigorous 
                         evaluations within the greedy context. Methods: We present a new 
                         greedy algorithm for unconstrained CIT, T-Tuple Reallocation 
                         (TTR), to generate CIT test suites specifically via the 
                         Mixed-value Covering Array (MCA) technique. The main reasoning 
                         behind TTR is to generate an MCA M by creating and reallocating 
                         t-tuples into this matrix M, considering a variable called goal 
                         (\ζ ). We performed two controlled experiments addressing 
                         cost-efficiency and only cost. Considering both experiments, we 
                         did 3200 executions related to 8 solutions. In the first 
                         controlled experiment, we compared versions 1.1 and 1.2 of TTR in 
                         order to check whether there is significant difference between 
                         both versions of our algorithm. In such experiment, we jointly 
                         considered cost (size of test suites) and efficiency (time to 
                         generate the test suites) in a multi-objective perspective. In the 
                         second controlled experiment we confronted TTR 1.2 with five other 
                         greedy algorithms/tools for unconstrained CIT: IPOG-F, jenny, 
                         IPO-TConfig, PICT, and ACTS. We performed two different 
                         evaluations within this second experiment where in the first one 
                         we addressed cost-efficiency (multi-objective) and in the second 
                         only cost (single objective). Results: Results of the first 
                         controlled experiment indicate that TTR 1.2 is more adequate than 
                         TTR 1.1 especially for higher strengths (5, 6). In the second 
                         controlled experiment, TTR 1.2 also presents better performance 
                         for higher strengths (5, 6) where only in one case it is not 
                         superior (in the comparison with IPOG-F). We can explain this 
                         better performance of TTR 1.2 due to the fact that it no longer 
                         generates, at the beginning, the matrix of t-tuples but rather the 
                         algorithm works on a t-tuple by t-tuple creation and reallocation 
                         into M. Conclusion: Considering the metrics we defined in this 
                         work and based on both controlled experiments, TTR 1.2 is a better 
                         option if we need to consider higher strengths (5, 6). For lower 
                         strengths, other solutions, like IPOG-F, may be better 
                         alternatives.",
                  doi = "10.1186/s40411-017-0043-z",
                  url = "http://dx.doi.org/10.1186/s40411-017-0043-z",
             language = "en",
           targetfile = "balera_algorithm.pdf",
        urlaccessdate = "19 abr. 2024"
}