Abstract: Software for producing and verifying computational determinations using a distributed ledger, by: (i) receiving an indication of a first artificial intelligence (AI) inferencing event, the first AI inferencing event including a first AI inferencing result produced by a first machine learning model based, at least in part, on a first input from a user; (ii) computing a hash of the first machine learning model using a cryptographic hash function; (iii) sending a record of the first AI inferencing event to a verification system, the record of the first AI inferencing event including the hash of the first machine learning model; and (iv) receiving a verification from the verification system indicating that the hash of the first machine learning model matches a hash of a second machine learning model and that the record of the first AI inferencing event has been stored in a first distributed ledger.

Abstract: Software for producing and verifying computational determinations using a distributed ledger, by: (i) receiving an indication of a first artificial intelligence (AI) inferencing event, the first AI inferencing event including a first AI inferencing result produced by a first machine learning model based, at least in part, on a first input from a user; (ii) computing a hash of the first machine learning model using a cryptographic hash function; (iii) sending a record of the first AI inferencing event to a verification system, the record of the first AI inferencing event including the hash of the first machine learning model; and (iv) receiving a verification from the verification system indicating that the hash of the first machine learning model matches a hash of a second machine learning model and that the record of the first AI inferencing event has been stored in a first distributed ledger.

Abstract: In an approach, one or more computer processors create a dictionary for each source code commit in a set of historical source code commits associated with a software deployment; create a similarity model based on the created dictionary for each source code commit in the set of historical source code commits; generate a vector embedding for a source code commit pair based on a set of log differences between source code commit pairs utilizing the created similarity model; generate, responsive to a new source code commit, a new vector embedding based on a set of log differences between the new source code commit and a preceding source code commit utilizing the created similarity model; generate a defect likelihood utilizing the generated new vector embedding; determine, responsive to the generated defect likelihood exceeding a defect likelihood threshold, that the new source code commit contains defects.

Abstract: Software for producing and verifying computational determinations using a distributed ledger, by: (i) receiving a first input from a user; (ii) producing a first computational determination utilizing a first computational model, wherein the first computational determination includes a first computational output that is based, at least in part, on the first input; (iii) computing a hash of the first computational model; (iv) sending a record of the first computational determination to a verification system, wherein the record of the first computational determination includes the hash of the first computational model; (v) receiving a verification from the verification system indicating that the hash of the first computational model matches a hash of a second computational model and that the record of the first computational determination has been stored in a first distributed ledger; and (vi) in response to receiving the verification, providing the first computational output to the user.

Abstract: An example operation may include one or more of transmitting a copy of the master ledger when a developer joins a network, receiving proposed changes to the master ledger, and updating the master ledger at the branch hash.

Abstract: An example operation may include one or more of seeking consensus among users of a source tree to approve execution of a blocked command to a source control system, creating a child ledger for a user issuing the blocked command and initiating a blockchain transaction to link the child ledger to a master ledger, merging the child ledger into the master ledger when consensus is reached, and executing the blocked command.

Abstract: An example operation may include one or more of transmitting a copy of the master ledger, wherein the master ledger comprises a code and history, receiving one or more changes to the copy of the master ledger, updating the master ledger when a required smart contract code standard is met, and initiating a push of the updated master ledger to one or more developer branches to enforce synchronization across the one or more developer branches.

Abstract: A method, system, and computer product for prompting a patient to take a medicine including collecting medicine prescription information associated with a patient, collecting medicine usage data associated with the patient, comparing the medicine usage data with the medicine prescription information, determining whether a medicine is taken by the patient as prescribed in the prescription information based on a comparison result of the medicine usage data and the prescription information, and generating one or more control signals to interrupt operations of one or more devices within an environment of the patient or environments of the patient's family member or caregivers, in response to determining that the medicine is not taken as prescribed.

Abstract: An example operation may include one or more of intercepting a command from a user to modify a source tree in a source control system, creating a child ledger associated with a master ledger when the intercepted command is destructive, seeking consensus among users of the source tree to approve execution of the intercepted command, merging the child ledger into the master ledger with a transaction describing the intercepted command, a commit tree history, and status of the consensus, and a tree hash, and merging execution results of the intercepted command into a history of the source tree.

Abstract: Software for immutably storing computational determinations using distributed ledgers. The software performs the following operations: (i) receiving an indication that a first computational model is ready to be deployed; (ii) storing a copy of the first computational model in a first distributed ledger; (iii) computing a hash of the first computational model using a cryptographic hash function; (iv) receiving an indication that a second computational model has been used to produce a first computational determination; (vi) receiving a hash of the second computational model; and (vii) in response to determining that the hash of the second computational model matches the hash of the first computational model, storing a record of the first computational determination in a second distributed ledger, wherein the record of the first computational determination identifies the second computational model as being the first computational model and includes the hash of the first computational model.

Abstract: A computer-implemented method, system and computer program product for identifying implicit dependencies between code artifacts. Co-defect association rules between code artifacts are generated, where such co-defect association rules include a prediction of how likely there will be a defect in a code artifact when there is a defect in an associated code artifact. After detecting a defect in a first code artifact, the co-defect association rules are reviewed to identify any code artifacts associated with the first code artifact. If there is a code artifact associated with the first code artifact, and if the probability of the associated code artifact being defected when the first code artifact is defected exceeds a threshold value, then a recommendation is made to the user to review not only the first code artifact that was defected but also its associated code artifact for a potential defect.

Abstract: A method, system and computer program product for detecting potential failures in completing a continuous delivery (CD) pipeline using machine learning. A CD pipeline is defined to include stages, where each stage includes a binary event(s). A model is created by applying an Apriori algorithm and a sequential pattern mining algorithm to a set of previous patterns of sequences of binary events to calculate confidence scores for completing a set of binary events in a particular order. After identifying an ongoing CD sequence (ordered set of binary events) for a software application, the model is used to predict a likelihood of the ongoing CD sequence for the software application completing the CD pipeline by generating confidence score(s) for the ongoing CD sequence. A notification is issued regarding a potential failure in completing the CD pipeline for the software application if a confidence score is below a threshold value.

Abstract: A method, system and computer program product for detecting potential failures in completing a continuous delivery (CD) pipeline using machine learning. A CD pipeline is defined to include stages, where each stage includes a binary event(s). A model is created by applying an Apriori algorithm and a sequential pattern mining algorithm to a set of previous patterns of sequences of binary events to calculate confidence scores for completing a set of binary events in a particular order. After identifying an ongoing CD sequence (ordered set of binary events) for a software application, the model is used to predict a likelihood of the ongoing CD sequence for the software application completing the CD pipeline by generating confidence score(s) for the ongoing CD sequence. A notification is issued regarding a potential failure in completing the CD pipeline for the software application if a confidence score is below a threshold value.

Abstract: Software for preventing fraud in digital content licensing and distribution using a distributed ledger technology. The software performs the following operations: (i) receiving a request for a license of a digital asset, wherein a record of the digital asset is stored in a first distributed ledger; (ii) verifying a consensus for the request of the license of the digital asset; and (iii) responsive to verifying the consensus for the request of the license of the digital asset: storing a transaction settlement record in a second distributed ledger, creating a sharded copy of the digital asset including a plurality of shards of the digital asset, and storing at least one shard of the sharded copy of the digital asset in the second distributed ledger with sharding instructions for reconstructing the digital asset from the sharded copy.

Abstract: In an approach, one or more computer processors create a dictionary for each source code commit in a set of historical source code commits associated with a software deployment; create a similarity model based on the created dictionary for each source code commit in the set of historical source code commits; generate a vector embedding for a source code commit pair based on a set of log differences between source code commit pairs utilizing the created similarity model; generate, responsive to a new source code commit, a new vector embedding based on a set of log differences between the new source code commit and a preceding source code commit utilizing the created similarity model; generate a defect likelihood utilizing the generated new vector embedding; determine, responsive to the generated defect likelihood exceeding a defect likelihood threshold, that the new source code commit contains defects.

Abstract: An example operation may include one or more of creating a child ledger for each new environment that is setup, updating a master ledger when a smart contract condition is met, and when the master ledger is updated, initiating deployment of an update to a plurality of managed environments.

Abstract: A computer-implemented method, system and computer program product for identifying implicit dependencies between code artifacts. Co-defect association rules between code artifacts are generated, where such co-defect association rules include a prediction of how likely there will be a defect in a code artifact when there is a defect in an associated code artifact. After detecting a defect in a first code artifact, the co-defect association rules are reviewed to identify any code artifacts associated with the first code artifact. If there is a code artifact associated with the first code artifact, and if the probability of the associated code artifact being defected when the first code artifact is defected exceeds a threshold value, then a recommendation is made to the user to review not only the first code artifact that was defected but also its associated code artifact for a potential defect.

Abstract: A method, system and computer program product for detecting potential failures in a continuous delivery pipeline. A machine learning model is created to predict whether changed portion of codes under development at various stages of the continuous delivery pipeline will result in a pipeline failure. After creating the machine learning model, log file(s) may be received that were generated by development tool(s) concerning a changed portion of code under development at a particular stage of the continuous delivery pipeline. The machine learning model provides relationship information between the log file(s) and the changed portion of code. A message is then generated and displayed based on this relationship information, where the message may provide a prediction or a recommendation concerning potential failures in the continuous delivery pipeline. In this manner, the potential failures in the continuous delivery pipeline may be prevented without requiring context switching.

Abstract: A method, system and computer program product for detecting software build errors. A classification system is created that identifies users' questions in crowdsource data pertaining to errors in computer programs that are associated with a log report. A model is built to classify log data as bug-related or not bug-related based on the classification system. Log reports from log data obtained from crowdsource data are identified as being bug-related based on the model. After vectorizing such log reports and storing the vectorized log reports, the language of a new build log report for a software product is vectorized upon completion of the build of the software product. If the vectorized log report is within a threshold amount of distance to a stored vectorized log report, then a copy of the log report (bug-related) and a source of the log report associated with the stored vectorized log report is provided.

Abstract: A system, a method and a computer program product for managing multiple meetings and/or conference calls by performing the steps of receiving, by a computer, invitations to attend, by at least one attendee, two or more meetings, and detecting, by the computer, a one or more conflicts between the two meetings. The computer communicates, to the attendee, at least one detected conflict of the one or more detected conflicts. The computer receives an identification of a single meeting of the two or more meetings the at least one attendee will attend and at least one meeting to be transcribed. At least one meeting to be transcribed includes all meetings of the two or more meetings exclusive of the single meeting the attendee will attend. At least a portion of the at least one meeting is transcribed into a first textual transcript and selectively delivered to the attendee in real time.