International Business Machines Corporation

Alessandro Morari

Jim Alain Laredo

Eun Kyung Lee

Yunhui Zheng

Yoonho Park

Bilge Acun

Giacomo Domeniconi

Saurabh Pujar

Luca Buratti

Yufan Zhuang

Marco Pistoia

Alper Buyuktosunoglu

Charles Ray Johns

Zhong Su

James Allan Kahle

Bo Yang

Martin Ohmacht

Shiwan Zhao

Alfio Massimiliano Gliozzo

Honglei Guo

Mihaela Ancuta Bornea

Shripad Nadgowda

James R Doran

Jose R Brunheroto

James C Sexton

Gaetano Rossiello

Abdullah Kayi

Guojing Cong

Jeffrey Scott McCarley

Changhoan Kim

Constantinos Evangelinos

Sahil Suneja

Patrick D Siegl

Chih-Chieh Yang

Daniele Buono

Fabio Checconi

Patrick Dm Siegl

Techniques regarding training one or more AI models for a source code understanding task are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a training component that can train an artificial intelligence model on source code samples for a source code understanding task. The source code samples can be ranked based on code complexity.

Complexity based artificial intelligence model training

Techniques regarding augmenting one or more training datasets for training one or more AI models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise training augmentation component that can generate an augmented training dataset for training an artificial intelligence model by extracting a simplified source code sample from a source code sample comprised within a training dataset.

Training data augmentation via program simplification

In a computer-implemented method for improving a static analyzer output, a processor receives a labeled data set with labeled true vulnerabilities and labeled false vulnerabilities. A processor receives pretrained contextual embeddings from a contextual embeddings model. A processor maps the true vulnerabilities and the false vulnerabilities to the pretrained contextual embeddings model. A processor generates a fine-tuned model with classifications for true vulnerabilities.

Contextual embeddings for improving static analyzer output

Approaches presented herein enable fault detection. More specifically, implementation code of one or more functions is identified from source code. The implementation code of the one or more functions is converted to corresponding Abstract Syntax Trees (ASTs). The implementation code of the one or more functions is represented as a first plurality of sets of AST paths over the ASTs. Classification results for the one or more functions are generated with a classifier based on the first plurality of sets of AST paths for the implementation code of the one or more functions. Each of the classification results indicates a probability of having at least one fault in a corresponding function of the one or more functions. Fault detection results of the source code are generated based on the classification results.

Source code fault detection

Based on historic job data, a computer processor can predict a configuration of a computer node for running a future computer job. The computer processor can pre-configure the computer node based on the predicted configuration. Responsive to receiving a submission of a job, the computer processor can launch the job on the pre-configured computer node.

Job-launch time reduction by node pre-configuration

A method provides for collecting data source images from multiple repositories. Application dependencies are discovered from the data source images. Status results are determined based on vulnerability and compliance scanning of all dependent sources for each data source image. The status results are aggregated across all data source images for each of the multiple repositories. Remediations are determined for violations indicated by the aggregated status results. Each of the remediations is aggregated and ordered to define a single global remediation solution.

Remediation strategy optimization for development, security and operations (DevSecOps)

A method, a computer system, and a computer program product for building pre-trained contextual embeddings is provided. Embodiments of the present invention may include collecting programming code. Embodiments of the present invention may include loading and preparing the programming code using a specialized programming language keywords-based vocabulary. Embodiments of the present invention may include creating contextual embeddings for the programming code. Embodiments of the present invention may include storing the contextual embeddings.

Building pre-trained contextual embeddings for programming languages using specialized vocabulary

A method for context-aware data mining of a text document includes receiving a list of words parsed and preprocessed from an input query; computing a related distributed embedding representation for each word in the list of words using a word embedding model of the text document being queried; aggregating the related distributed embedding representations of all words in the list of words to represent the input query with a single embedding, by using one of an average of all the related distributed embedding representations or a maximum of all the related distributed embedding representations; retrieving a ranked list of document segments of N lines that are similar to the aggregated word embedding representation of the query, where N is a positive integer provided by the user; and returning the list of retrieved segments to a user.

NLP-based context-aware log mining for troubleshooting

An approach is disclosed that maintains a consistent view of a virtual address by a local node which writes a first value to the virtual address and, after writing the first value, establishes a snapshot consistency state of the virtual address. The virtual address is shared amongst any number of processes and the processes includes a writing process and other processes that read from the virtual address. After writing the first value, the writing process writes a second value to the virtual address. Even after writing the second value, the first value is still visible to the other processes.

Global virtual address space consistency model

A data index sequence indexing a dataset is received. A location of a data sample identified by a data index in the data index sequence is determined. A scheme is generated for specifying a data movement based on the location. Responsive to determining that the location is a cache of a process, the data sample in the cache can be reused without having to load the data sample from a storage device.

Locality aware data loading for machine learning

A plurality of hardware accelerators are interconnected and include a special processing unit and accelerator memory. At least one host computer is coupled to each of the plurality of hardware accelerators and includes a general processing unit and host memory. The plurality of hardware accelerators exchange data in a ring communication pattern in computing a linear layer of a neural network.

Hardware environment and method of performing matrix multiplication in artificial intelligence applications

An application to run on a computer node comprising a plurality of hardware components is received. Expected performance of the hardware components is received. A power shifting ratio associated with each of the plurality of hardware components for each phase of the application is determined. Power between the hardware components is dynamically shifted based on the power shifting ratio at different phases of the application.

Variation-aware intra-node power shifting among different hardware components

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New York, NY, United States of America

Alessandro Morari