Automatic Static Analysis Tools (ASATs) detect coding rule violations, including mistakes and bad practices that frequently occur during programming. While ASATs are widely used in both OSS and industry, the developers do not resolve more than 80% of the detected violations. As one of the reasons, most ASATs users do not customize their ASATs to their projects after installation; the ASATs with the default configuration report many rule violations that confuse developers. To reduce the ratio of such uninteresting warning messages, we propose a method to customize ASATs according to the product source code automatically. Our fundamental hypothesis is: A software project has interesting ASAT rules that are consistent over time. Our method takes source code as input and generates an ASAT configuration. In particular, the method enables optional (i.e., disabled by default) rules that detected no violations on the version because developers are likely to follow the rules in future development. Our method also disables violated rules because developers were unlikely to follow them. To evaluate the method, we applied our method to 643 versions of four JavaScript projects. The generated configurations for all four projects increased the ASAT precision. They also increased recall for two projects. The result shows that our method helps developers to focus on their attractive rule violations. Our implementation of the proposed method is available at https://github.com/devreplay/linter-maintainer

The importance of developing a support system for medical decision-making in the clinical field has been pointed out for improving the accuracy and objectivity of the judgment of clinicians, and for early detection of disease. In this paper, we notice that signs, symptoms and clinical laboratory data are essential information which show the functional depression and failure of the ecosystem, and a practical software system is presented that provides physicians with clues to clinical diagnosis. First, by analysing the medical task and the structuralization of patient information, a medical modelling method is provided, where diagnosis can be considered as the identification of a patient who corresponds to the controlled object. Secondly, by using the concept of a patient model and a disease model, a system identification algorithm is proposed, and an actual system constructed for medical diagnosis is described in order to confirm the usefulness of the proposed method.