International Business Machines Corporation

Alexander Sobran

Bo Zhang

Bradley C Herrin

Xianjun Zhu

John H Walczyk, Iii

Joseph Paul Hughes

Guilherme Ferreira

Halley Fede

David Wehr

Eleanor Pence

ChunHui Yang Higgins

William Patrick Higgins

Pu Yang

Ke Zhu

Isabella Yamin

Darrough West

Yogesh Rane

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.

Producing and verifying computational determinations using a distributed ledger

Provided are a computer program product, system, and method for classifying code as introducing a bug or not introducing a bug to train a bug detection algorithm. For each commit in a commit history of code changes to the code base, a determination is made of lines of code changed by the commit. For each line of code changed by the commit, a determination is made as to whether the commit is for a bug fix. A determination is made as to whether a previous commit changing the line of code changed by the commit for the bug fix in response to determining that the commit is for the bug fix. Indication is made that the previous commit introduced a bug. The algorithm is trained to classify changes to lines of code by commits indicated as having introduced a bug as bug introducing commits.

Classifying code as introducing a bug or not introducing a bug to train a bug detection algorithm

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.

Vector embedding of relational code sets

A computer-implemented method, system and computer program product for representing source code in vector space. The source code is parsed into an abstract syntax tree, which is then traversed to produce a sequence of tokens. Token embeddings may then be constructed for a subset of the sequence of tokens, which are inputted into an encoder artificial neural network ('encoder') for encoding the token embeddings. A decoder artificial neural network ('decoder') is initialized with a final internal cell state of the encoder. The decoder is run the same number of steps as the encoding performed by the encoder. After running the decoder and completing the training of the decoder to learn the inputted token embeddings, the final internal cell state of the encoder is used as the code representation vector which may be used to detect errors in the source code.

Representing source code in vector space to detect errors

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.

Approaches presented herein enable domain knowledge-based software defect prediction. More specifically, a cost function configured to train a machine learning model to predict a defect in a software version is obtained. A domain knowledge penalty metric is then determined for a software development team associated with the software version. A linear combination of the determined domain knowledge penalty metric is then applied to the obtained cost function to create a domain knowledge-modified cost function. Using this domain knowledge-modified cost function, the machine learning model is then trained based on a minimization of the domain knowledge-modified cost function. Once trained, the machine learning model is used to report a set of predicted values based on the domain knowledge-modified cost function trained machine learning model.

Devops driven cognitive cost function for software defect prediction

A method, system and computer program product for obtaining vector representations of code snippets capturing semantic similarity. A first and second training set of code snippets are collected, where the first training set of code snippets implements the same function representing semantic similarity and the second training set of code snippets implements a different function representing semantic dissimilarity. A vector representation of a first and second code snippet from either the first or second training set of code snippets is generated using a machine learning model. A loss value is generated utilizing a loss function that is proportional or inverse to the distance between the first and second vectors in response to receiving the first and second code snippets from the first or second training set of code snippets, respectively. The machine learning model is trained to capture the semantic similarity in the code snippets by minimizing the loss value.

Generating vector representations of code capturing semantic similarity

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.

Blockchain technique for immutable source control

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.

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.

Immutably storing computational determinations using distributed ledgers

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.

Providing insight of continuous delivery pipeline using machine learning

The present invention relates to a method, system, and computer program product for project adoption documentation generation using machine learning. A method includes receiving a set of project parameters for a set of projects. In an embodiment, a method includes receiving a set of input project parameters for an input project. In an embodiment, a method includes comparing each set of project parameters to the set of input project parameters. In an embodiment, a method includes selecting, responsive to the comparison, a project from the set of projects. In an embodiment, a method includes generating, responsive to the selection, documentation for the input project.

Project adoption documentation generation using machine learning

A system and method for recommending whether to upgrade dependencies of a software project includes mining historical software data artifacts associated with a plurality of software projects to extract a plurality of metrics and dependency version lineages from the historical software data artifacts, clustering the software projects based on the metrics and the dependency version lineages, extracting target metrics and a target version lineage of a target software project selecting a software project cluster from the plurality of clusters that best matches the target software project, analyzing the metrics for each of the software projects included in the at least one software project cluster to determine that a measurable change to the metrics occurred as a result of upgrading dependencies of the software projects, and recommending which software dependencies of the target software project should be upgraded based on the measurable change to the metrics of the software projects.

Recommending software project dependency upgrades

Token embedding based on target-context pairs includes obtaining a structural representation of data, the structural representation including nodes and indicating relationships between the nodes, obtaining a context template that identifies relationship(s) to use in identifying a respective context for different nodes of the structural representation, applying the context template to the structural representation to obtain a set of target-context pairs, each of which includes a respective target node of the structural representation and a respective set of context node(s), of the structural representation, for that target node, as identified based on the context template, and using the target-context pairs in a model and obtaining, as output of the model, representations of target nodes of the target-context pairs as vectors in a vector space.

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Chapel Hill, NC, United States of America

Alexander Sobran