A machine-learning model task deduction method, system, and computer program product include extracting data schema of a machine-learning model and analyzing the data schema to determine an intended task of the machine-learning model.

A machine-learning model task deduction method, system, and computer program product include extracting data schema of a machine-learning model and analyzing the data schema to determine an intended task of the machine-learning model.

One example method of operation may include identifying a code segment accessed during a code access event, scanning code of the code segment to identify a degree of sensitivity of the code, appending a code access annotation to the code segment based on detected actions performed to the code during the code access event, and updating a code log to identify the code access annotation.

A transfer learning system is used for the development of neural transformer models pertaining to software engineering tasks. The transfer learning system trains source code domain neural transformer models with attention in various configurations on a large corpus of unsupervised training dataset of source code programs and/or source code-related natural language text. A web service provides the trained models for use in developing a model that may be fine-tuned on a supervised training dataset associated with a software engineering task thereby generating a tool to perform the software engineering task.

Techniques are described herein that are capable of performing quality-based action(s) regarding engineer-generated documentation associated with code and/or an API. Features are extracted from data associated with the engineer-generated documentation, which includes engineer-generated document(s). Weights are assigned to the features. The quality-based action(s) are performed. The quality-based action(s) include generating quality score(s) for the engineer-generated document(s) and/or providing a recommendation to revise a subset of the engineer-generated document(s). Each quality score is based at least in part on the weights assigned to the features that correspond to the respective engineer-generated document. The recommendation recommends performance of an operation to increase the quality of each engineer-generated document in the subset based at least in part on the weights assigned to the features that correspond to the subset.

Techniques are described herein that are capable of performing quality-based action(s) regarding engineer-generated documentation associated with code and/or an API. Features are extracted from data associated with the engineer-generated documentation, which includes engineer-generated document(s). Weights are assigned to the features. The quality-based action(s) are performed. The quality-based action(s) include generating quality score(s) for the engineer-generated document(s) and/or providing a recommendation to revise a subset of the engineer-generated document(s). Each quality score is based at least in part on the weights assigned to the features that correspond to the respective engineer-generated document. The recommendation recommends performance of an operation to increase the quality of each engineer-generated document in the subset based at least in part on the weights assigned to the features that correspond to the subset.

Provided is a specification document creation system including a first computer and a second computer, wherein the first computer is configured to generate, based on configuration information, display data for displaying a first display item, which is determined by the configuration information, of a BIOS on a display device, the configuration information indicating a hardware configuration of the first computer, and analyze the display data to generate first information including the first display item included in the display data, and the second computer is configured to analyze a source code of the BIOS to generate second information including a second display item, which is determined independently of the hardware configuration of the first computer, of the BIOS, and create a specification document for the BIOS in which the first display item included in the first information and the second display item included in the second information are described.

Methods, computer program products, and/or systems are provided that perform the following operations: reading lines of code included in a collection of source code; identifying an assigned tag in the collection of source code; identifying a start tag location and an end tag location associated with the assigned tag, wherein the lines of code included between the start tag location and the end tag location identify a code block; processing the code block to generate logging data for the code block based, at least in part, on the assigned tag; and providing the logging data for linking to executable byte code compiled from the collection of source code, wherein the logging data is used to log code data relating to the code block during execution of the executable byte code.

Methods, computer program products, and/or systems are provided that perform the following operations: reading lines of code included in a collection of source code; identifying an assigned tag in the collection of source code; identifying a start tag location and an end tag location associated with the assigned tag, wherein the lines of code included between the start tag location and the end tag location identify a code block; processing the code block to generate logging data for the code block based, at least in part, on the assigned tag; and providing the logging data for linking to executable byte code compiled from the collection of source code, wherein the logging data is used to log code data relating to the code block during execution of the executable byte code.

While a compiler compiles source code to create an executable binary, code is added into the compiled source code that, when executed, identifies and stores in a metadata table base and bounds information associated with memory allocations. Additionally, additional code is added into the compiled source code that performs memory safety checks during execution. This updated compiled source code automatically determines a safety of memory access requests during execution by performing an out-of-bounds (OOB) check using the base and bounds information retrieved and stored in the metadata table. This enables the identification and avoidance of unsafe memory operations during the implementation of the executable by a GPU.