Abstract: The disclosure discloses a method and an apparatus for determining code generation quality and efficiency evaluation values based on multiple indicators. The method includes that: an object code is acquired; an interactive data set is acquired; a multidimensional evaluation indicators is determined and characteristic values corresponding to each piece of data in the interactive data set are extracted; characteristic values corresponding to each piece of data in the interactive data set are determined as a characteristic data set; a multidimensional syntax tree is constructed; feature learning is performed on the multidimensional syntax tree to obtain feature parameters; the evaluation parameter model is controlled to output an evaluation value. According to the disclosure, the evaluation method in a related art is to evaluate a static automatic code generation result, ignoring the influence of code changes on the evaluation result, resulting in low reliability of the evaluation result is solved.

Abstract: The disclosure discloses a method and an apparatus for determining code generation quality and efficiency evaluation values based on multiple indicators. The method includes that: an object code is acquired; an interactive data set is acquired; a multidimensional evaluation indicators is determined and characteristic values corresponding to each piece of data in the interactive data set are extracted; characteristic values corresponding to each piece of data in the interactive data set are determined as a characteristic data set; a multidimensional syntax tree is constructed; feature learning is performed on the multidimensional syntax tree to obtain feature parameters; the evaluation parameter model is controlled to output an evaluation value. According to the disclosure, the evaluation method in a related art is to evaluate a static automatic code generation result, ignoring the influence of code changes on the evaluation result, resulting in low reliability of the evaluation result is solved.

Abstract: Examples of the present disclosure describe systems and methods for visit prediction using machine learning (ML) attribution techniques. In aspects, data relating to users and their venue visits is collected and merged with data relating to various directed information impressions. Features of the merged data are identified for one or more time intervals and assigned values and/or labels. The identified features and corresponding values/labels may be used to train an ML model to provide a visit probability for each user represented in the merged data.