International Business Machines Corporation

Dell Products Inc.

Dell & Products L.p.

Kumar Saurav

Jagabondhu Hazra

Manikandan Padmanaban

Ranjini Bangalore Guruprasad

Isaac Waweru Wambugu

Ivan Kayongo

Abhishek Mishra

Vivek Bhargava

Lloyd Alan Treinish

Sharada Desai

An apparatus comprises a processing device configured to determine specifications for an information technology asset to be developed, and to identify, utilizing at least one machine learning model, whether at least one of the specifications for the information technology asset is defect-prone, wherein a given specification is identified as defect-prone responsive to at least one output of the at least one machine learning model indicating that the given specification has at least a threshold likelihood of resulting in one or more defects during development of the information technology asset. The processing device is also configured to establish a mapping between the one or more identified defect-prone specifications for the information technology asset and one or more components of the information technology asset, and to modify one or more development processes for the information technology asset based at least in part on the established mapping.

Machine-learning based prediction of defect-prone components of information technology assets

Methods, apparatus, and processor-readable storage media for generating test cases for software testing using machine learning techniques are provided herein. An example computer-implemented method includes obtaining user input data associated with at least one software application; identifying one or more predetermined types of information, including one or more of topic-related information and entity-related information, from at least a portion of the user input data using at least a first set of one or more machine learning techniques; generating one or more test cases for testing at least a portion of the at least one software application by processing at least a portion of the identified information using at least a second set of one or more machine learning techniques; and performing one or more automated actions based at least in part on at least one of the one or more generated test cases.

Generating test cases for software testing using machine learning techniques

A method, computer system, and a computer program product for improving environmental impact estimations is provided. The present invention may include obtaining data pertaining to an agricultural area. The present invention may include deriving one or more features from the data pertaining to the agricultural area. The present invention may include identifying one or more stubble burning areas within the agricultural areas based on the one or more derived features. The present invention may include determining an environmental impact for each of the one or more stubble burning areas.

Enhancing greenhouse gas emission estimates for stubble burning areas

Methods, systems, and computer program products for generating context-aware process-based model determinations for greenhouse gas emissions from agricultural fields are provided herein. A computer-implemented method includes obtaining data related to multiple conditions pertaining to at least one agricultural field; deriving one or more contextual features for one or more activities associated with the at least one agricultural field, wherein deriving the contextual feature(s) includes processing at least a portion of the obtained data using one or more activity-related models; updating one or more greenhouse gas emission estimates, pertaining to the at least one agricultural field, generated by at least one process-based model by processing at least a portion of the one or more greenhouse gas emission estimates and at least a portion of the derived contextual feature(s) using a spatio-temporal learning model; and performing one or more automated actions based on the one or more updated greenhouse gas emission estimates.

Generating context-aware process-based model determinations for greenhouse gas emissions from agricultural fields

Methods, systems, and computer program products for enhancing spatial and temporal resolution of greenhouse gas emission estimates for agricultural fields using cohort analysis techniques are provided herein. A computer-implemented method includes obtaining non-greenhouse gas remote sensing data and contextual information pertaining to agricultural fields; determining cohorts among the agricultural fields by deriving agricultural field-specific features from the obtained data and contextual information; computing agricultural field-level time series of greenhouse gas emission estimates for the cohorts by processing the obtained data and contextual information using a process-based model; calculating bias corrections for the cohorts by processing, using a time series learning model, the time series and background greenhouse gas emission estimates; generating resolution-enhanced greenhouse gas emission estimates for the cohorts based on initial greenhouse gas emission estimates derived from greenhouse gas remote sensing data and the calculated bias corrections; and performing automated actions based on the updated greenhouse gas emission estimates.

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Bengaluru, India

Kumar Saurav