Dynamic evaluation and improvement of energy efficiency of computer code

A system comprising:

• one or more memories, and
• one or more computers, coupled to the one or more memories, configured to:
• obtain code data characterizing a computer code snippet to be executed by a computing device;
• obtain rule data characterizing one or more energy efficiency rules;
• generate a first input from the code data and a second input from the rule data;
• process the first input using a first embedding machine-learning model to generate a first embedding feature vector for the computer code snippet;
• process the second input using a second embedding machine-learning model to generate a second embedding feature vector for the one or more energy efficiency rules;
• process (i) the first embedding feature vector and (ii) the second embedding feature vector using a code defect detection machine-learning model to generate a detection output comprising data representing one or more portions of the computer code snippet that negatively impact a dynamic energy efficiency of the computer code snippet upon execution by the computing device;
• and
• perform an action based on the detection output,
• wherein each of the first embedding machine-learning model and the second embedding machine-learning model comprises a respective language model, and
• wherein the first embedding machine-learning model comprises a first Long Short-Term Memory (LSTM) network with a higher drop rate than a second LSTM network of the second embedding machine-learning model.

A non-transitory computer-readable storage media storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:

• obtaining code data characterizing a computer code snippet to be executed by a computing device;
• obtaining rule data characterizing one or more energy efficiency rules;
• generating a first input from the code data and a second input from the rule data;
• processing the first input using a first embedding machine-learning model to generate a first embedding feature vector for the computer code snippet;
• processing the second input using a second embedding machine-learning model to generate a second embedding feature vector for the one or more energy efficiency rules;
• processing (i) the first embedding feature vector and (ii) the second embedding feature vector using a code defect detection machine-learning model to generate a detection output comprising data representing one or more portions of the computer code snippet that negatively impact a dynamic energy efficiency of the computer code snippet upon execution by the computing device;
• and
• performing an action based on the detection output,
• wherein each of the first embedding machine-learning model and the second embedding machine-learning model comprises a respective language model, and
• wherein the first embedding machine-learning model comprises a first Long Short-Term Memory (LSTM) network with a higher drop rate than a second LSTM network of the second embedding machine-learning model.