System and method for determining potential issues with a software deployment
Systems, devices, methods, and computer readable media for determining potential issues with a software upgrade are disclosed. In one implementation, the disclosed system includes at least one processor and at least one non-transitory memory storing instructions to perform operations when executed by the at least one processor. The operations include training a machine learning model on historic trouble ticket data; receiving data relating to a planned software upgrade; providing the received data to the trained machine learning model, wherein the trained machine learning model generates a tag value; calculating a risk score based on the tag value; and determining a client-specific action to be performed relating to the planned software upgrade and based on the risk score.
1 . A system for determining potential issues with a software upgrade, comprising:
at least one processor; and
at least one non-transitory memory storing instructions to perform operations when executed by the at least one processor including:
training a machine learning model on historic trouble ticket data, wherein the historic trouble ticket data includes issues raised in connection with a prior software upgrade, how the raised issues were resolved, and client sentiment regarding the issues raised and their resolution;
receiving data relating to a planned software upgrade;
providing the received data to the trained machine learning model, wherein:
the trained machine learning model generates a tag value;
the tag value is associated with a trouble ticket; and
the tag value is used to assign the trouble ticket to a class of potential client impact categories based on historical trouble ticket patterns;
calculating a risk score for the trouble ticket based on information in the trouble ticket and the associated tag value, wherein calculating the risk score includes:
determining a likelihood that the planned software upgrade will cause client-reported problems based on similarity to historical software upgrade patterns; and
correlating the planned software upgrade with historical client-reported issues; and
determining an action plan to be performed relating to the planned software upgrade and based on the risk score, wherein the action plan includes generating client-specific communications based on historical client sentiment analysis derived from historical trouble ticket resolutions.
2 . The system of claim 1 , wherein the historic trouble ticket data includes software update data, bug tracking data, source code host data, and project management data.
3 . The system of claim 1 , wherein the received data includes software update data, source code host data, and project management data.
4 . The system of claim 1 , wherein providing the received data to the trained machine learning model includes performing one or more of: data ingestion; extraction, transformation, and loading; natural language processing; and time matching.
5 . The system of claim 4 , wherein performing time matching includes matching a time of an historic software upgrade with a time of bug tracking data and a time of project management data.
6 . The system of claim 1 , wherein the instructions further include:
classifying potential issues with the planned software upgrade based on the tag value.
7 . The system of claim 6 , wherein the risk score is based on a classification and the tag value.
8 . The system of claim 1 , wherein the instructions further include:
comparing the risk score to a first threshold; and
on a condition that the risk score exceeds the first threshold, determining the action plan to be performed relating to the planned software upgrade and based on the risk score.
9 . The system of claim 8 , wherein the instructions further include:
calculating a sentiment score based on prior client communications;
on a condition that the sentiment score exceeds a second threshold and is less than a third threshold, generating a client communication by a trained second machine learning model regarding the planned software upgrade; and
on a condition that the sentiment score exceeds the second threshold and the third threshold, generating and sending an alert to a client support team to contact the client.
10 . The system of claim 1 , wherein the action plan is generated by a second machine learning model, the second machine learning model having been trained on historical communications with the client.
11 . A computer-implemented method for determining potential issues with a software upgrade, comprising:
training a machine learning model on historic trouble ticket data, wherein the historic trouble ticket data includes issues raised in connection with a prior software upgrade, how the raised issues were resolved, and client sentiment regarding the issues raised and their resolution;
receiving data relating to a planned software upgrade;
providing the received data to the trained machine learning model, wherein:
the trained machine learning model generates a tag value;
the tag value is associated with a trouble ticket; and
the tag value is used to assign the trouble ticket to a class of potential client impact categories based on historical trouble ticket patterns;
calculating a risk score for the trouble ticket based on information in the trouble ticket and the associated tag value, wherein calculating the risk score includes:
determining a likelihood that the planned software upgrade will cause client-reported problems based on similarity to historical software upgrade patterns; and
correlating the planned software upgrade with historical client-reported issues; and
determining an action plan to be performed relating to the planned software upgrade and based on the risk score, wherein the action plan includes generating client-specific communications based on historical client sentiment analysis derived from historical trouble ticket resolutions.
12 . The method of claim 11 , wherein the historic trouble ticket data includes software update data, bug tracking data, source code host data, and project management data.
13 . The method of claim 11 , wherein the received data includes software update data, source code host data, and project management data.
14 . The method of claim 11 , wherein providing the received data to the trained machine learning model includes performing one or more of: data ingestion; extraction, transformation, and loading; natural language processing; and time matching.
15 . The method of claim 14 , wherein performing time matching includes matching a time of an historic software upgrade with a time of bug tracking data and a time of project management data.
16 . The method of claim 11 , further comprising:
classifying potential issues with the planned software upgrade based on the tag value.
17 . The method of claim 16 , wherein the risk score is based on a classification and the tag value.
18 . The method of claim 11 , further comprising:
comparing the risk score to a first threshold; and
on a condition that the risk score exceeds the first threshold, determining the action plan to be performed relating to the planned software upgrade and based on the risk score.
19 . The method of claim 18 , further comprising:
calculating a sentiment score based on prior client communications;
on a condition that the sentiment score exceeds a second threshold and is less than a third threshold, generating a client communication by a trained second machine learning model regarding the planned software upgrade; and
on a condition that the sentiment score exceeds the second threshold and the third threshold, generating and sending an alert to a client support team to contact the client.
20 . A non-transitory computer-readable storage medium comprising instructions for determining potential issues with a software upgrade, wherein when executed by a processor perform operations comprising:
training a machine learning model on historic trouble ticket data, wherein the historic trouble ticket data includes issues raised in connection with a prior software upgrade, how the raised issues were resolved, and client sentiment regarding the issues raised and their resolution;
receiving data relating to a planned software upgrade;
providing the received data to the trained machine learning model, wherein:
the trained machine learning model generates a tag value;
the tag value is associated with a trouble ticket; and
the tag value is used to assign the trouble ticket to a class of potential client impact categories based on historical trouble ticket patterns;
calculating a risk score for the trouble ticket based on information in the trouble ticket and the associated tag value, wherein calculating the risk score includes:
determining a likelihood that the planned software upgrade will cause client-reported problems based on similarity to historical software upgrade patterns; and
correlating the planned software upgrade with historical client-reported issues; and
determining an action plan to be performed relating to the planned software upgrade and based on the risk score, wherein the action plan includes generating client-specific communications based on historical client sentiment analysis derived from historical trouble ticket resolutions.
21 . The non-transitory computer-readable storage medium of claim 20 , wherein providing the received data to the trained machine learning model includes performing one or more of: data ingestion; extraction, transformation, and loading;
natural language processing; and time matching.
22 . The non-transitory computer-readable storage medium of claim 21 , wherein performing time matching includes matching a time of an historic software upgrade with a time of bug tracking data and a time of project management data.
23 . The non-transitory computer-readable storage medium of claim 20 , wherein the operations further comprise:
comparing the risk score to a first threshold; and
on a condition that the risk score exceeds the first threshold, determining the action plan to be performed relating to the planned software upgrade and based on the risk score.
24 . The non-transitory computer-readable storage medium of claim 23 , wherein the operations further comprise:
calculating a sentiment score based on prior client communications;
on a condition that the sentiment score exceeds a second threshold and is less than a third threshold, generating a client communication by a trained second machine learning model regarding the planned software upgrade; and
on a condition that the sentiment score exceeds the second threshold and the third threshold, generating and sending an alert to a client support team to contact the client.