Abstract: A method for checking the dynamic behavior of code generated using a language model. Th method includes: providing a first executable file from a program code generated using a language model; providing a second executable file, wherein the second executable file is a previous first executable file or is an original source code of the program code; executing differential fuzzing using a fuzzer, wherein the fuzzer injects identical inputs into the first executable file and into the second executable file; monitoring the behavior and the output of the first executable file and the second executable file; outputting the program code if the fuzzing found no inconsistencies, no errors and/or no worse runtime behavior of the first executable file compared to the second executable file.

Abstract: A method for improving the memory allocation of code generated using a language model. The method includes: providing a program code generated using a language model, if a new version of the program code is available; generating an executable file using compilation and instrumentation, wherein a memory sanitizer inserts instructions into the program code and/or the executable file; execution of fuzzing by a fuzzer, wherein the fuzzer injects inputs into the executable file; monitoring the memory performance and optionally runtime information, the behavior and/or the output of the executable file; storing metadata generated from the allocated and freed memory in a memory metadata database, wherein the metadata are based on the instructions and are stored when the executable file is generated and/or when the fuzzing is executed; outputting the program code if no memory performance degradation or other errors are found.

Abstract: A method for verifying static warnings of code generated by a language model includes (i) providing an executable file from a program code generated by a language model, (ii) providing warning points in the program code originating from static testing, (iii) performing directed fuzzing by a fuzzer, wherein the fuzzer injects inputs into the executable file to reach a warning point, (iv) monitoring the behavior and output of the executable file, and (v) rating the warning point based on the behavior and the output.

Abstract: A computer-implemented method for the automated generation of test code for testing software. The method includes generating, via a machine learning model, at least one test case and/or a test code at least based on a code of the software and a prompt; and evaluating the at least one test case and/or the test code, wherein an evaluation result is obtained. A computer-implemented method for further training a machine learning model and/or further machine learning model is also described. The machine learning model configured to generate at least one test case and/or a test code for testing software at least based on a code of the software and a prompt, and the further machine learning model is designed to generate a test code for testing the software at least based on at least one test case and a further prompt.

Abstract: A method for automatically translating program code from a source language to a target language. The method includes: translating a source program code in a source language into a target program code in a target language by means of a language model; repeating the translation with changed conditions, such as changing one or more hyperparameters such as a temperature parameter of the language model, transformations in the source program code, and/or changes in the input to the language model; comparing the source program code and the target program code or codes with a test harness, wherein the test harness is generated automatically; and evaluating the target program code based on code quality metrics, test quality metrics and/or the number of tests.

Abstract: A computer-implemented method for the automated generation of a model of computation. The method includes generating, via a machine learning model, at least one model of computation based at least on a specification that the at least one model of computation is to fulfill, and a prompt; and evaluating the at least one model of computation, resulting in an evaluation result. A computer-implemented method for further training a machine learning model, wherein the machine learning model is designed to generate at least one model of computation is also described. The method includes adapting the machine learning model at least based on at least one model of computation and at least one evaluation result, wherein the at least one evaluation result results from evaluating the at least one model of computation.

Abstract: A vibronic measuring device, e.g. limit level sensor, for determining and/or monitoring at least one process variable, includes a mechanically oscillatory unit, which is excited to vibrate by at least one drive unit based on an electrical signal SA. A receiving unit receives and converts mechanical vibrations into an electrical signal SE. A control and evaluation unit applies closed- and/or open-loop control of the vibrational excitation, and evaluates the signal SE with respect to the process variable. A vibration sensor pick up a sensor signal SS at the vibration sensor, and an analysis unit is supplied with signals SE and SS, and applies self-learning analysis of the input signals and transmits reliability information for the signal SE to the control and evaluation unit.

Abstract: A computer-implemented method for training a machine learning model for evaluating a behavior of an autonomous system that is configured to solve a perception task. The method includes: receiving sensor data by a perception system of the autonomous system; receiving metadata, wherein the metadata encodes an influence on a solvability of the perception task; ascertaining an error probability based on the received sensor data; providing the machine learning model, which is designed to map sensor data and metadata to an error probability for solving the perception task; and training the machine learning model based on a training data set element comprising the received sensor data, the received metadata and the ascertained error probability. A computer-implemented method for evaluating a behavior of an autonomous system that is configured to solve a perception task, is also described.

Abstract: A method for checking the performance of a prediction task by a neural network. The method includes: supplying image data to a feature extraction network that is trained to determine a representation of the image data; supplying the determined representation to a prediction network; a first determination of an optical flow between the image data and further image data from the representation; a second determination of the optical flow between the image data and the further image data from the image data using a different calculation path; and comparing the result of the first determination of the optical flow with the result of the second determination of the optical flow and accepting a result of the prediction task as correct if the result of the first determination of the optical flow agrees with the result of the second determination of the optical flow within a predefined tolerance.

Abstract: A method for evaluating a data set with regard to suitability for determining a calculation function of a virtual sensor includes providing the data set. The data set includes measurement data resulting from a measurement of measured variables by at least two real sensors. The measurement data has a particular dimension for one of the at least two real-world sensors. The method further includes providing an input range defined for the measured variables of the at least two real sensors to specify at least one requirement for determining the calculation function. The method further includes determining a coverage ratio between the data set and the provided input range using a machine learning model, and evaluating the data set based on the determined coverage ratio.

Abstract: A computer-implemented method for testing conformance between images generated by a synthetic image generator and images obtained from authentic visual data. A conformance test result results from comparing results of global sensitivity analyses used to rank the effect of visual parameters on the computer vision model both for synthetic and authentic visual data.

Abstract: A method for optimized training of a machine learning algorithm. The method includes: providing a domain model that has domain parameters and/or domain values for at least one domain; providing a data model that has a training data set; removing and/or hiding and/or modifying at least one training datum from the training data set depending on at least one domain parameter and/or domain value to provide a reduced training data set; training a neural network based on the reduced training data set to determine a model performance depending on the reduced data set; comparing the determined model performance with a model performance and determining a model performance deviation depending on the reduced data set; selecting training data from the training data set depending on the model performance deviation; training the machine learning algorithm based on the selected training data; and providing the trained machine learning algorithm.

Abstract: A method for optimized training of a machine learning algorithm. The method includes: providing a domain model that has domain parameters and/or domain values for at least one domain; providing a data model that has a training data set including training data for the at least one domain; removing/hiding/modifying at least one training datum from the training data set depending on at least one domain parameter and/or domain value to provide a reduced training data set; training a neural network based on the reduced training data set to determine a model performance depending on the reduced data set; comparing the determined model performance with a model performance associated with the training data set; selecting training data from the training data set depending on the comparison of the model performances; training the machine learning algorithm based on the selected training data; and providing the trained machine learning algorithm.

Abstract: A method determines a distance metric for determining a distance to a data point having heterogeneous classes of variables. The method includes providing training records each assigning a label to a data point, the training records partitioned into training data points of a training amount and validation data points of a validation amount, and training a data-based system model with the training amount, such that the data-based system model associates data points with a model output, respectively. The method further includes for each validation data point of the validation amount, determining a quality level of the data-based system model and a distance value to a nearest training data point for each of the heterogeneous classes of variables. The distance value to the nearest training data point is determined separately with respect to a respective class of variables.

Abstract: A method for preprocessing code data for a subsequent evaluation, preferably for a safety-critical application. The method includes: providing a representation of the code data, wherein the representation has a multitude of paths, which specify different sequences of syntactic elements of a code of the code data; selecting a plurality of paths from the multitude of paths for the subsequent evaluation, wherein the paths are selected in a uniformly distributed manner, wherein a number of the selected paths is lower than a total number of the multitude of paths; carrying out a path calculation in which the selected paths are calculated, wherein the path calculation is limited to the selected paths; providing the calculated paths for the evaluation.

Abstract: A method for operating an injection valve by determining an opening or closing time of the injection valve based on a sensor signal. The method includes: providing an evaluation point time series by sampling a sensor signal of a sensor of the injection valve; using a non-linear data-based first sub-model to obtain a first output vector based on the evaluation point time series, wherein each element of the first output vector is associated with a specific time; using a linear, data-based second sub-model to obtain a second output vector based on the evaluation point time series, wherein each element of the second output vector is associated with a specific time; limiting the time determined by the first output vector depending on the second output vector in order to obtain the opening or closing time.

Abstract: A method for training a data-based evaluation model to determine an opening or closing time of an injection valve based on a sensor signal. The method includes: measuring an operation of the injection valve in order to determine at least one sensor signal and an associated opening or closing time; sampling the sensor signal at a sampling rate in order to obtain a sensor signal time series with sensor signal values; determining a plurality of training data sets by assigning a plurality of evaluation point time series generated from a sensor signal time series to the opening or closing time associated with the sensor signal, wherein the evaluation point time series has a lower temporal resolution than the sensor signal time series; training the data-based evaluation model depending on the determined training data sets.

Abstract: Modifying a visual parameter specification characterising the operational design domain of the computer vision model by improving the visual parameter specification according to a sensitivity analysis of the computer vision model.

Abstract: Reducing the number of parameters in a visual parameter set based on a sensitivity analysis of how a given visual parameter affects the performance of a computer vision model to provide a verification parameter set having a reduced size.

Abstract: A computer-implemented method for generating reliability indication data of a computer vision model. The method includes: obtaining visual data including an input image or sequence representing an observed scene, the visual data being characterizable by a first set of visual parameters; analysing the observed scene in the visual data using a computer vision reliability model sensitive to a second set of visual parameters, the second set of visual parameters includes a subset of the first set of visual parameters, and is obtained from the first set of visual parameters according to a sensitivity analysis applied to a plurality of parameters in the first set of visual parameters, the sensitivity analysis is performed during an offline training phase of the computer vision reliability model; generating reliability indication data of the observed scene using the analysis of the observed scene; and outputting the reliability indication data of the computer vision model.