Disclosed embodiments are directed at systems, methods, and architecture for providing auto-documentation to APIs. The auto documentation plugin is architecturally placed between an API and a client thereof and parses API requests and responses in order to generate auto-documentation. In some embodiments, the auto-documentation plugin is used to update preexisting documentation after updates. In some embodiments, the auto-documentation plugin accesses an on-line documentation repository. In some embodiments, the auto-documentation plugin makes use of a machine learning model to determine how and which portions of an existing documentation file to update.

Disclosed embodiments are directed at systems, methods, and architecture for providing auto-documentation to APIs. The auto documentation plugin is architecturally placed between an API and a client thereof and parses API requests and responses in order to generate auto-documentation. In some embodiments, the auto-documentation plugin is used to update preexisting documentation after updates. In some embodiments, the auto-documentation plugin accesses an on-line documentation repository. In some embodiments, the auto-documentation plugin makes use of a machine learning model to determine how and which portions of an existing documentation file to update.

Embodiments of the invention are directed to automatically capturing user interface screenshots for use in documentation of a software product. Aspects include identifying a user interface window of the software product and creating a degree-of-completion graph for the user interface window. Aspects also include capturing a plurality of screenshots of the user interface window during use of the software product and calculating a degree-of-completion percentage for each of the plurality of screenshots. Aspects further include identifying a subset of the plurality of screenshots to be included in the software product documentation based on the degree-of-completion percentage.

Embodiments of the invention are directed to automatically capturing user interface screenshots for use in documentation of a software product. Aspects include identifying a user interface window of the software product and creating a degree-of-completion graph for the user interface window. Aspects also include capturing a plurality of screenshots of the user interface window during use of the software product and calculating a degree-of-completion percentage for each of the plurality of screenshots. Aspects further include identifying a subset of the plurality of screenshots to be included in the software product documentation based on the degree-of-completion percentage.

Aspects of the invention include receiving, by a processor, source code for a software program written in a first programming language. The received source code is converted into abstracted source code that is in a generic format that is different than a format of the first programming language. The abstracted source code is compared to known source code patterns. Based on determining that at least a subset of the abstracted source code matches a pattern in the known source code patterns, sending an alert to the user indicating that the received source code matches the pattern.

Aspects of the invention include receiving, by a processor, source code for a software program written in a first programming language. The received source code is converted into abstracted source code that is in a generic format that is different than a format of the first programming language. The abstracted source code is compared to known source code patterns. Based on determining that at least a subset of the abstracted source code matches a pattern in the known source code patterns, sending an alert to the user indicating that the received source code matches the pattern.

A method and system may be provided for recording discussions about computer code in an integrated development environment (“IDE”). In some aspects, a communication channel is integrated with an IDE. Communications and discussions may be tracked and linked with specific code sections.

Various computing technologies for various reverse engineering platforms capable of outputting, including creating or generating, a human readable and high level source code, such as C, Fortran, LISP, or BASIC, from various binary files, such as application binaries, executable binaries, or data binaries, in an original language as developed pre-compilation. For example, some of such reverse engineering platforms can be programmed to disassemble binary files from different process architectures, identify various code optimizations as compiler introduced, reverse or unwind various compiler optimizations (de-optimize), and generate a human readable and high-level source code from de-optimized data.

Various computing technologies for various reverse engineering platforms capable of outputting, including creating or generating, a human readable and high level source code, such as C, Fortran, LISP, or BASIC, from various binary files, such as application binaries, executable binaries, or data binaries, in an original language as developed pre-compilation. For example, some of such reverse engineering platforms can be programmed to disassemble binary files from different process architectures, identify various code optimizations as compiler introduced, reverse or unwind various compiler optimizations (de-optimize), and generate a human readable and high-level source code from de-optimized data.

The present application discloses methods, systems, and computer program products for automatically generating source code implementing a regular expression. A regular expression that is defined within source code of an application project is identified. The source code uses a higher-level programming language. Based on identifying the regular expression, a source code implementation of the regular expression is automatically generated using the higher-level programming language, and the source code implementation of the regular expression is integrated into the application project. After integrating the source code implementation of the regular expression into the application project, the source code implementation of the regular expression is compiled into lower-level code and the lower-level code is emitted into an application executable when building the application project, or the source code implementation of the regular expression is interpreted when executing the application project.