Abstract This paper illustrates the power of extending Language Driven Engineering (LDE) with Domain-Specific Natural Languages (DSNLs) through a case study on two levels. Both cases benefit from the characteristic decomposition feature of LDE, resulting in tasks tailored to the application of domain-specific languages, here with a focus on the application of DSNLs supported by LLM-based code generation. In the first case study, we show how DSNL-supported LDE facilitates the development of point-and-click adventures, whereas the second case study focuses on migration: We demonstrate how the entire LDE scenario for point-and-click adventure games can be migrated to output TypeScript instead of JavaScript using LLM-based code generation exclusively, without manually writing any code. This migration not only infers the required types, but also preserves an important property of the original LDE scenario: generated web applications can be automatically validated by design via automata learning and subsequent model checking. Even better, this property can be exploited to automatically validate the correctness of the migration by learning so-called difference automata that characterize the behavioral differences between the generated JavaScript-based and Type-Script-based applications.

Abstract In this paper, we present Forest GUMP (for Generalized, Unifying Merge Process) a tool for verification and precise explanation of Random forests. Besides pre/post-condition-based verification and equivalence checking, Forest GUMP also supports three concepts of explanation, the well-known model explanation and outcome explanation, as well as class characterization, i.e., the precise characterization of all samples that are equally classified. Key technology to achieve these results is algebraic aggregation, i.e., the transformation of a Random Forest into a semantically equivalent, concise white-box representation in terms of Algebraic Decision Diagrams (ADDs). The paper sketches the method and demonstrates the use of Forest GUMP along illustrative examples. This way readers should acquire an intuition about the tool, and the way how it should be used to increase the understanding not only of the considered dataset, but also of the character of Random Forests and the ADD technology, here enriched to comprise infeasible path elimination. As Forest GUMP is publicly available all experiments can be reproduced, modified, and complemented using any dataset that is available in the ARFF format.

Abstract In this paper, we present Forest GUMP (for Generalized, Unifying Merge Process) a tool for providing tangible experience with three concepts of explanation. Besides the well-known model explanation and outcome explanation, Forest GUMP also supports class characterization, i.e., the precise characterization of all samples with the same classification. Key technology to achieve these results is algebraic aggregation, i.e., the transformation of a Random Forest into a semantically equivalent, concise white-box representation in terms of Algebraic Decision Diagrams (ADDs). The paper sketches the method and illustrates the use of Forest GUMP along an illustrative example taken from the literature. This way readers should acquire an intuition about the tool, and the way how it should be used to increase the understanding not only of the considered dataset, but also of the character of Random Forests and the ADD technology, here enriched to comprise infeasible path elimination.