Error recovery
A system and method may provide assistance to programmer during programming to detect and predict the existence of errors in code and, in some aspects, predict fixes for erroneous code. In some aspects, the system and method may use artificial intelligence to learn based on edits made by programmers, by observing code changes that cause errors and code changes that fix errors, or based on other data.
1. A computer-implemented method comprising:
monitoring a communication channel, the communication channel comprising information of an execution environment, compiler, or interpreter, the execution environment configured to execute computer code;
parsing the information in the communication channel and identifying an error, wherein the information comprises computer code and standard output, execution state, execution status, logs or streams of data generated as a result of execution, compiling or interpreting of the computer code;
identifying and storing error text and error context of the error, wherein the error context comprises portions of computer code responsible for the error, configuration settings, or execution state at the time of the error;
extracting one or more features from the error text and error context;
predicting, based on the one or more extracted features, one or more error types of the error, wherein the predicting comprises determining a likelihood for each of the one or more error types;
classifying, based on the determined likelihood of the one or more error types, the error into one or more error types;
predicting, based on the error type, error text and error context, a suggested fix, wherein the suggested fix comprises one or more corrected code segments;
incorporating the suggested fix in the computer code; and
determining whether the error was handled successfully by the incorporating of the suggested fix.
2. The computer-implemented method of claim 1 , wherein identifying an error in the content of the communication channel is performed by a machine learning algorithm.
3. The computer-implemented method of claim 2 , wherein the machine learning algorithm is a neural network.
4. The computer-implemented method of claim 1 , wherein the error context comprises a stack trace.
5. The computer-implemented method of claim 4 , wherein the stack trace indicates a portion of source code and the error context further includes the indicated portion of source code.
6. The computer-implemented method of claim 1 , wherein evaluating an error status of the error is performed by a machine learning algorithm.
7. The computer-implemented method of claim 6 , wherein the machine learning algorithm is a neural network.