Author summaryThe available strategy for controlling the diseases transmitted by Aedes ægypti (dengue fever, Zika, and chikungunya) relies on continued community participation. Despite slogans emphasizing how easy it should be, no country has achieved it since the seventies. To better investigate potentially sustainable interventions, we developed a systemic model based on a multidisciplinary approach, integrating as deeply as possible specialized knowledge and field experience. The resulting model is composed of 4 external and 8 internal subsystems and 31 relationships, consistent with the literature and checked over multiple iterations with specialists of the many areas. We analyzed the model and the main feedback loops responsible for the system’s stability, searching for possible interventions that could shift the existing balance. We suggest the introduction of 1 more player, the local primary health care structure, with the potential to change the undesired equilibrium. The health agents in the areas are the first to detect disease cases, and they could stimulate individuals to inform about potential mosquitoes’ breeding sites and bring timely information to the vector-control program. Triggering such an action could introduce changes in people's attitude through a positive feedback loop in the desired direction.

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Mutation testing is a program-transformation technique that injects artificial bugs to check whether the existing test suite can detect them. However, the costs of using mutation testing are usually high, hindering its use in industry. Useless mutants (equivalent and duplicated) contribute to increase costs. Previous research has focused mainly on detecting useless mutants only after they are generated and compiled. In this paper, we introduce a strategy to help developers with deriving rules to avoid the generation of useless mutants. To use our strategy, we pass as input a set of programs. For each program, we also need a passing test suite and a set of mutants. As output, our strategy yields a set of useless mutants candidates. After manually confirming that the mutants classified by our strategy as "useless" are indeed useless, we derive rules that can avoid their generation and thus decrease costs. To the best of our knowledge, we introduce 37 new rules that can avoid useless mutants right before their generation. We then implement a subset of these rules in the MUJAVA mutation testing tool. Since our rules have been derived based on artificial and small Java programs, we take our MUJAVA version embedded with our rules and execute it in industrial-scale projects. Our rules reduced the number of mutants by almost 13% on average. Our results are promising because (i) we avoid useless mutants generation; (ii) our strategy can help with identifying more rules in case we set it to use more complex Java programs; and (iii) our MUJAVA version has only a subset of the rules we derived.

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