Abstract: According to an aspect of an embodiment, operations may include receiving a first data point associated with a real-time application and predicting a first class for the received first data point, by a Deep Neural Network (DNN) pre-trained for a classification task of the real-time application. The operations may further include extracting, from the DNN, a first set of features and a corresponding first set of weights, for the predicted first class. The extracted first set of features may be associated with a convolution layer of the DNN. The operations may further include determining, by a pre-trained classifier associated with the predicted first class, a confidence score for the predicted first class based on the extracted first set of features and the corresponding first set of weights. The operations may further include generating output information to indicate correctness of the predicted first class based on the determined confidence score.

Abstract: According to one or more embodiments, operations may include identifying a plurality of source code edits made between a buggy version of first source code of a first software program and a repaired version of the first source code. The operations may also include identifying a plurality of clusters of the source code edits. Each cluster of the plurality of clusters includes one or more source code edits of the plurality of source code edits. The operations may also include identifying a plurality of valid clusters of the plurality of clusters and identifying one or more invalid clusters of the plurality of clusters based on compiling of the first source code. Moreover, the operations may include identifying, as a first repair of the first error, a particular set of one or more valid clusters of the plurality of valid clusters while excluding the one or more invalid clusters from consideration.

Abstract: Operations may include obtaining a code artifact in which the code artifact is a code snippet is of a software language of interest. The operations may also include identifying fault locations of the code snippet that each correspond to a parsing error. In addition, the operations may include implementing repairs for each identified fault location in which, for each fault location, a respective repair is implemented for its corresponding parsing error such that the code snippet is modified into a parsable code snippet.

Abstract: Operations may include generating a first graphical representation of a buggy version of source code of a software program and generating a second graphical representation of a repaired version of the source code. The buggy version may include an error and the error has been corrected in the repaired version. The operations may also include identifying, as matching nodes, a first node of the first graphical representation and a second node of the second graphical representation that match each other. The operations may additionally include applying a same reduction to the first node and to the second node concurrently to generate a reduced buggy version of the source code and a reduced repaired version of the source code. Further, the operations may include generating a repair example with respect to correcting the error in which the repair example is based on the reduced buggy version and the reduced repaired version.

Abstract: Operations may include obtaining a code artifact in which the code artifact is a code snippet is of a software language of interest. The operations may also include identifying fault locations of the code snippet that each correspond to a parsing error. In addition, the operations may include implementing repairs for each identified fault location in which, for each fault location, a respective repair is implemented for its corresponding parsing error such that the code snippet is modified into a parsable code snippet.

Abstract: A method may include obtaining a first violation in a first software program and identifying one or more characteristics of the first violation. The method may include searching at least one website to identify a set of posts. The method may include obtaining, as a first context, a first portion of the first software program that includes the first violation. The method may include obtaining a set of second contexts, each second context corresponding to a different post of the set of posts. The method may also include determining a set of similarity scores by comparing a first abstract tree representation of the first context with each second abstract tree representation corresponding with the second contexts and selecting a particular post based on the similarity score between the first abstract tree representation and the particular second abstract tree representation associated with the particular post.

Abstract: A method may include obtaining a first violation in a first portion of a first software program and obtaining a first proposed patch to remediate the first violation. The method may include identifying a second software program with a second portion that includes a second violation. The method may include simplifying the second portion of the second software program by removing one or more elements in the second portion that are identified as extraneous. The method may include applying the first proposed patch for the first violation to the simplified second portion to generate a repaired simplified second portion. The method may include obtaining an executable repaired simplified second portion from the repaired simplified second portion. The method may include presenting the second violation and the executable repaired simplified second portion as an example of how the first proposed patch would affect the first violation and the first software program.

Abstract: A method may include obtaining a first violation in a first portion of a first software program and obtaining a first proposed patch to remediate the first violation. The method may include identifying a second software program with a second portion that includes a second violation. The method may include simplifying the second portion of the second software program by removing one or more elements in the second portion that are identified as extraneous. The method may include applying the first proposed patch for the first violation to the simplified second portion to generate a repaired simplified second portion. The method may include obtaining an executable repaired simplified second portion from the repaired simplified second portion. The method may include presenting the second violation and the executable repaired simplified second portion as an example of how the first proposed patch would affect the first violation and the first software program.

Abstract: A method may include obtaining a first violation in a first software program and identifying one or more characteristics of the first violation. The method may include searching at least one website to identify a set of posts. The method may include obtaining, as a first context, a first portion of the first software program that includes the first violation. The method may include obtaining a set of second contexts, each second context corresponding to a different post of the set of posts. The method may also include determining a set of similarity scores by comparing a first abstract tree representation of the first context with each second abstract tree representation corresponding with the second contexts and selecting a particular post based on the similarity score between the first abstract tree representation and the particular second abstract tree representation associated with the particular post.

Abstract: A method of automated candidate repair patch generation may include synthesizing repair expressions based on project code. Concise repair expressions may be mined from a code database. A set of repair expressions may be generated based on the synthesized repair expressions and the mined repair expressions. The set of repair expressions may include further repair expressions further synthesized from the synthesized repair expressions and the mined repair expressions. Candidate patches may be generated based on a repair schema and the set of repair expressions.

Abstract: A method may include obtaining a deep neural network model and obtaining a first training data point and a second training data point for the deep neural network model during a first training epoch. The method may include determining a first robustness value of the first training data point and a second robustness value of the second training data point. The method may further include omitting augmenting the first training data point in response to the first robustness value satisfying a robustness threshold and augmenting the second training data point in response to the second robustness value failing to satisfy the robustness threshold. The method may also include training the deep neural network model on the first training data point and the augmented second training data point during the first training epoch.

Abstract: According to an aspect of an embodiment, operations may include generating a first abstract syntax tree with respect to a first iteration of first source code of a first software program and generating a second abstract syntax tree with respect to a second iteration of the first source code. The operations may also include identifying a first sub-tree of the first abstract syntax tree and identifying a plurality of second sub-trees of the second abstract syntax tree. In addition the operations may include generating a first textual representation of the first sub-tree and generating a plurality of second textual representations with respect to the second sub-trees. Moreover, the operations may include modifying the second abstract syntax tree based on the second sub-trees and obtaining a third iteration of the first source code by regenerating the first source code based on the modified second abstract syntax tree.

Abstract: A method may include obtaining multiple lines of programming code of a program, and obtaining multiple test cases for testing the program, where each of the test cases includes an assertion upon which a result of a respective test case is based. The method may also include executing the program for each of the test cases, and identifying affected lines of programming code that influence the assertions. The method may additionally include calculating a risk score for at least one of the lines of programming code based on the affected lines of programming code and the assertion, the risk score indicative of a likelihood that the at least one of the lines of programming code includes a fault.

Abstract: According to an aspect of an embodiment, operations may include generating a first abstract syntax tree with respect to a first iteration of first source code of a first software program and generating a second abstract syntax tree with respect to a second iteration of the first source code. The operations may also include identifying a first sub-tree of the first abstract syntax tree and identifying a plurality of second sub-trees of the second abstract syntax tree. In addition the operations may include generating a first textual representation of the first sub-tree and generating a plurality of second textual representations with respect to the second sub-trees. Moreover, the operations may include modifying the second abstract syntax tree based on the second sub-trees and obtaining a third iteration of the first source code by regenerating the first source code based on the modified second abstract syntax tree.

Abstract: A method of automated candidate repair patch generation may include synthesizing repair expressions based on project code. Concise repair expressions may be mined from a code database. A set of repair expressions may be generated based on the synthesized repair expressions and the mined repair expressions. The set of repair expressions may include further repair expressions further synthesized from the synthesized repair expressions and the mined repair expressions. Candidate patches may be generated based on a repair schema and the set of repair expressions.

Abstract: Operations may include identifying an edit that corresponds to a fault of a program. The edit may be identified based on a difference between the program and an improved program. The operations may include obtaining an AST that represents the program and includes defect nodes that correspond to a fault location and include an edit node. The operations may include classifying a particular node as a primary node that operates as a starting point for the edit. The operations may include identifying a path from the primary node to the edit node. The operations may include generating a fix pattern based on the path and the edit. The operations may include performing repairs with respect to an identified fault of code. The repairs may be performed using the fix pattern and based on the identified fault of the code being the same type as the fault of the program.

Abstract: A method may include obtaining a forwarding rule associated with a network. The method may also include identifying a property associated with the network. The property may include a property value comprising a duration value. Additionally, the method may include validating, for the forwarding rule, the property in the network. Responsive to the property being valid for the forwarding rule, the method may include updating the duration value based on a first equation set. Responsive to the property being invalid for the forwarding rule, the method may include updating the duration value based on a second equation set. Furthermore, the method may include calculating a length of validity value of the property. The length of validity value may be based on the updated duration value. The method may include performing network monitoring. The network monitoring may be based on the length of validity value of the property.

Abstract: A method may include obtaining multiple lines of programming code of a program, and obtaining multiple test cases for testing the program, where each of the test cases includes an assertion upon which a result of a respective test case is based. The method may also include executing the program for each of the test cases, and identifying affected lines of programming code that influence the assertions. The method may additionally include calculating a risk score for at least one of the lines of programming code based on the affected lines of programming code and the assertion, the risk score indicative of a likelihood that the at least one of the lines of programming code includes a fault.

Abstract: A method may include obtaining packet handling rules from at least one firewall in a network and at least one routing table in the network, and translating the packet handling rules to canonical data structures based on priority of rules at a given routing table or a given firewall. Each canonical data structure may represent a subset of packets affected by one or more corresponding packet handling rules such that each packet handling rule is covered by at least one canonical data structure. The method may also include generating a graph representation of the firewalls and the nodes corresponding to the routing tables in the network. The method may additionally include labeling vertices and edges in the graph representation based on the packet handling rules. The method may also include, using the graph representation, verifying one or more network properties to identify any network issues.

Abstract: According to an aspect of an embodiment, a method may include executing multiple tests with respect to code under test of a software program to perform multiple test executions. The method may further include identifying one or more passing tests and one or more failing tests of the test executions. In addition, the method may include determining an aggregated score for each statement based on two or more of: the passing tests and the failing tests; a semantic similarity between one or more statement tokens included in the respective statement and one or more report tokens included in an error report; and an amount of time that has passed from when the respective statement received a change. Moreover, the method may include identifying a particular statement of the plurality of statements as a fault location in the code under test based on the aggregated scores of the plurality of statements.