ERROR CHECKING FOR CODE
Apparatuses, systems and methods are provided for checking code for errors. The apparatuses, systems and methods may send a target code and a prompt for code checking to a machine learning (ML) chatbot to cause the ML chatbot to check the target code for errors. The apparatuses, systems and methods may determine whether there is an error in the target code based at least partially on a response from the ML chatbot. The apparatuses, systems and methods may, responsive to determining that there is an error in the target code, determine, via an interaction with the ML chatbot, whether there is a solution to fix the error. The apparatuses, systems and methods may, responsive to determining that there is a solution to fix the error, cause the ML chatbot to (i) fix the error, and/or (ii) present the error and/or the solution to a user.
1 . A computer system for checking errors for a target code to be used in an insurance application, the computer system comprising:
one or more processors;
a memory storing executable instructions thereon that, when executed by the one or more processors, cause the one or more processors to:
send the target code and a prompt for code checking to a machine learning (ML) chatbot to cause the ML chatbot to check the target code for errors,
determine whether there is an error in the target code based at least partially on a response from the ML chatbot,
responsive to determining that there is an error in the target code, determine, via an interaction with the ML chatbot, whether there is a solution to fix the error, and
responsive to determining that there is a solution to fix the error, cause the ML chatbot to
(i) fix the error to obtain corrected code, and/or
(ii) present the error and/or the solution to a user.
2 . The computer system of claim 1 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
responsive to determining that there is no solution to fix the error, send the error to a human expert, and
receive corrected code via input from the human expert.
3 . The computer system of claim 1 , wherein to determine whether there is an error, the instructions, when executed by the one or more processors, further cause the one or more processors to:
cause the ML chatbot to check the target code with test cases for errors.
4 . The computer system of claim 3 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
(a) cause the ML chatbot to check the corrected code for further errors with additional test cases,
(b) responsive to further detected errors, send the further detected errors to the ML chatbot and determine whether there is a solution to fix the error,
(c) responsive to determining that there is a solution to fix the error, cause the ML chatbot to fix the further detected error, and
repeat (a) to (c) until no further errors are detected and/or a number of repeating (a) to (c) exceeds a predetermined repetition number.
5 . The computer system of claim 4 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
responsive to there being further errors and the repetition exceeding a predetermined repetition number, send the code and the further detected errors to a human expert.
6 . The computer system of claim 3 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
generate the test cases based upon data collected from one or more past or current users.
7 . The computer system of claim 3 , wherein the test cases are generated by the ML chatbot.
8 . The computer system of claim 1 , wherein to determine whether there is an error, the instructions, when executed by the one or more processors, further cause the one or more processors to execute the target code with test cases to check for errors.
9 . The computer system of claim 1 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
replace the target code in the insurance application with the corrected code to generate a new version of insurance application.
10 . The computer system of claim 9 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
implement the new version of the insurance application; and
initiate an integration test to check for errors or unintended consequences with implanting the new version of the insurance application.
11 . The computer system of claim 9 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
send the new version of the insurance application to the ML chatbot; and
cause the ML chatbot to check the new version of the insurance application for errors or unintended consequences.
12 . The computer system of claim 9 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
send the new version of the insurance application to the ML chatbot; and
cause the ML chatbot to evaluate the new version of the insurance application for security risks.
13 . The computer system of claim 1 , to present the solution to the user, the instructions, when executed by the one or more processors, cause the one or more processors to:
send, to the user, a file presenting the solution, the file is in at least one of (i) an audio format and (ii) a video format.
14 . The computer system of claim 13 , wherein to send the file in at least one of (i) the audio format and (ii) the video format, the executable instructions, when executed by the one or more processors, cause the one or more processors to:
convert the solution to a set of executable instructions,
implement the set of executable instructions with the target code to generate an implementation result;
based upon the implementation result, determine whether the error is fixed;
responsive to determining that the error is fixed, generate at least one of (i) an audio file and (ii) a video file based upon the implementation of the set of executable instructions.
15 . A computer-implemented method for checking errors for a target code to be used in an insurance application, the method comprising:
sending, by one or more processors, the target code and a prompt for code checking to a machine learning (ML) chatbot to cause the ML chatbot to check the target code for errors,
determining, by the one or more processors, whether there is an error in the target code based at least partially on a response from the ML chatbot,
responsive to determining that there is an error in the target code, determining, by the one or more processors and via an interaction with the ML chatbot, whether there is a solution to fix the error, and
responsive to determining that there is a solution to fix the error, causing, by the one or more processors, the ML chatbot to
(i) fix the error to obtain corrected code, and/or
(ii) present the error and/or the solution to a user.
16 . The method of claim 15 , wherein determining whether there is an error includes:
causing, by the one or more processors, the ML chatbot to check the target code with test cases for errors.
17 . The method of claim 16 , further comprising:
(a) causing, by the one or more processors, the ML chatbot to check the corrected code for further errors with additional test cases,
(b) responsive to further detected errors, sending, by the one or more processors, the further detected errors to the ML chatbot and determine whether there is a solution to fix the error,
(c) responsive to determining that there is a solution to fix the error, causing, by the one or more processors, the ML chatbot to fix the further detected error, and
repeating, by the one or more processors, (a) to (c) until no further errors are detected and/or a number of repeating (a) to (c) exceeds a predetermined repetition number.
18 . The method of claim 15 , further comprising:
executing, by the one or more processors, the target code with test cases to check for errors.
19 . The method of claim 15 , further comprising:
replacing, by the one or more processors, the target code in the insurance application with the corrected code to generate a new version of insurance application.
20 . A computer readable storage medium comprising non-transitory computer readable instructions stored thereon for checking errors for a target code to be used in an insurance application, wherein the instructions when executed on one or more processors cause the one or more processors to:
send the target code and a prompt for code checking to a machine learning (ML) chatbot to cause the ML chatbot to check the target code for errors,
determine whether there is an error in the target code based at least partially on a response from the ML chatbot,
responsive to determining that there is an error in the target code, determine, via an interaction with the ML chatbot, whether there is a solution to fix the error, and
responsive to determining that there is a solution to fix the error, cause the ML chatbot to
(i) fix the error, and/or
(ii) present the error and/or the solution to a user.