International Business Machines Corporation

Babita Sharma

Jeffrey Charles Turnham

John Thomas Peyton, Jr

Richard Myer Goldberg

Shu Wang

Kristofer Alyn Duer

Jason N Todd

Hua Xiao

David E Stewart

Andrew Mak

Matthew Francis Murphy

Stephen Darwin Teilhet

Ettore Merlo

Mathieu Merineau

Embodiments are disclosed for testing source code changes. The techniques include generating an incremental intermediate representation of a security vulnerability fix to repair an identified security vulnerability of a source code application. The techniques also include merging the incremental intermediate representation with a full intermediate representation of a previous version of the source code application. The techniques further include generating an impact graph based on the merged intermediate representation. Additionally, the techniques include performing a security vulnerability analysis on the security vulnerability fix based on the merged intermediate representation, the impact graph, and the identified security vulnerability. Further, the techniques include updating the security vulnerability analysis by removing one or more findings that are not related to the impact graph. Also, the techniques include generating a security assessment for the security vulnerability fix based on the updated security vulnerability analysis.

Testing source code changes

A computer-implemented method includes obtaining, by a processor, existing security information for static application security testing (SAST). The method also includes using, by the processor, the existing security information to discover, by a machine capable of learning, new security information. The method also includes improving, by the processor, security of a computer using the new security information.

Applying machine learning techniques to discover security impacts of application programming interfaces

A computer-implemented method for security scanning application code includes executing, via a processor, a full scan of the application code and generating a program intermediate representation (IR) and a list of security findings determined by the full scan. The processor executes an incremental scan of the application code after at least one change to the application code, and identifies at least one changed file in the application code. The processor then generates an incremental intermediate representation (IR) based at least in part on the at least one changed file. The processor merges the saved scan state and the incremental IR, produces a merged scan state, and outputs security findings based at least in part on the merged scan state and the incremental IR.

Scan time reduction in application code security scanning

An application analysis platform enables automatic generation of abstract program representations (APRs) that are amenable to static analyses for finding security vulnerabilities. The APR is generated automatically, preferably from an existing build system or a source repository, and then encapsulated into a binary archival format for consumption by a static analysis tool, which operates on-premises or in the cloud. The abstract program representation is a highly compact version of the actual source code it represents. The archival format obfuscates the source code that is subjected to the analysis, thus protecting it from being reverse-engineered when moved off-premises or otherwise shared with other users, teams and even organizations. Binary archive files generated separately from different source code components may be readily merged and analyzed together to provide more effective static data-flow analysis, even with respect to components that are built on different machines by different teams and at different times.

Static analysis based on abstract program representations

An application analysis platform enables automatic generation of abstract program representations (APRs) that are amenable to static analyzes for finding security vulnerabilities. The APR is generated automatically, preferably from an existing build system or a source repository, and then encapsulated into a binary archival format for consumption by a static analysis tool, which operates on-premises or in the cloud. The abstract program representation is a highly compact version of the actual source code it represents. The archival format obfuscates the source code that is subjected to the analysis, thus protecting it from being reverse-engineered when moved off-premises or otherwise shared with other users, teams and even organizations. Binary archive files generated separately from different source code components may be readily merged and analyzed together to provide more effective static data-flow analysis, even with respect to components that are built on different machines by different teams and at different times.

A cloud-based static analysis security tool that is accessible by a set of application development environments is augmented to provide for anonymous knowledge sharing to facilitate reducing security vulnerabilities. To the end, a crowdsourcing platform and social network are associated with the application development environments. Access to the social network platform by users of the application development environments is enabled. The anonymous access enables users to post messages without exposing sensitive data associated with a particular application development environment. As the static analysis security tool is used, a knowledgebase of information regarding identified security findings, fix priorities, and so forth, is continuously updated. Social network content (e.g., in the form of analytics, workflow recommendations, and the like) is then published from the knowledgebase to provide users with security knowledge generated by the tool from the set of application development environments. The approach provides for secure and anonymous cross-organization information sharing.

Crowd-sourced security analysis

A cloud-based static analysis security tool accessible by a set of application development environments is augmented to provide for anonymous knowledge sharing to facilitate reducing security vulnerabilities. To the end, a crowdsourcing platform and social network are associated with the application development environments. Access to the social network platform by users of the application development environments is enabled. The anonymous access enables users to post messages without exposing sensitive data associated with a particular application development environment. As the static analysis security tool is used, a knowledgebase of information regarding identified security findings, fix priorities, and so forth, is continuously updated. Social network content (e.g., in the form of analytics, workflow recommendations, and the like) is then published from the knowledgebase to provide users with security knowledge generated by the tool from the set of application development environments. The approach provides for secure and anonymous cross-organization information sharing based, for example, on analytics generated by an analytics platform.

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Mississauga, Canada

Babita Sharma