Abstract: A computer-implemented method includes training a classification model to produce a mapping from a set of feature vectors to a set of confidence levels. Each feature vector describes a respective term, and each confidence level indicates a likelihood that the respective term is a keyword. A plurality of program artifacts are tokenized into a plurality of terms. For each term in the plurality of terms, a respective feature vector is determined for describing the term, based on the plurality of program artifacts. For each term in the plurality of terms, based on the respective feature vector, using the mapping, a respective confidence level is determined for indicating a likelihood that the term is a keyword of the plurality of program artifacts.

Abstract: A computer-implemented method for generating an application programming interface (API) includes receiving metadata describing one or more source files of a program. The source files define a plurality of modules, and a first module is selected from among the plurality of modules. A chain of modules is traversed beginning at the first module, based on the metadata, where the chain of modules represents nested module calls described in the metadata. The traversing includes maintaining a set of common include files that are common to each module visited in the traversal of the chain of modules. Each include file in the set of common include files is placed into at least one of a request file list and a response file list. An API is generated for the module by submitting the set of common include files, the request file list, and the response file list to an API generator.

Abstract: A computer-implemented method for generating an application programming interface (API) includes receiving metadata describing one or more source files of a program. The source files define a plurality of modules, and a first module is selected from among the plurality of modules. A chain of modules is traversed beginning at the first module, based on the metadata, where the chain of modules represents nested module calls described in the metadata. The traversing includes maintaining a set of common include files that are common to each module visited in the traversal of the chain of modules. Each include file in the set of common include files is placed into at least one of a request file list and a response file list. An API is generated for the module by submitting the set of common include files, the request file list, and the response file list to an API generator.

Abstract: A computer-implemented method includes training a classification model to produce a mapping from a set of feature vectors to a set of confidence levels. Each feature vector describes a respective term, and each confidence level indicates a likelihood that the respective term is a keyword. A plurality of program artifacts are tokenized into a plurality of terms. For each term in the plurality of terms, a respective feature vector is determined for describing the term, based on the plurality of program artifacts. For each term in the plurality of terms, based on the respective feature vector, using the mapping, a respective confidence level is determined for indicating a likelihood that the term is a keyword of the plurality of program artifacts.

Abstract: According to one or more embodiments a computer-implemented method includes detecting, by a processor, an instruction to capture runtime data of a set of instructions in a computer program, the instruction detected based on a hook associated with the set of instructions. The method further includes determining whether the hook is an active expiring hook. The method further includes in response to the hook being an active expiring hook, determining a time condition associated with the expiring hook. The method further includes in response to the time condition being met, capturing the runtime data of the set of instructions in the computer program.

Abstract: According to one or more embodiments a computer-implemented method includes detecting, by a processor, an instruction to capture runtime data of a set of instructions in a computer program, the instruction detected based on a hook associated with the set of instructions. The method further includes determining whether the hook is an active expiring hook. The method further includes in response to the hook being an active expiring hook, determining a time condition associated with the expiring hook. The method further includes in response to the time condition being met, capturing the runtime data of the set of instructions in the computer program.

Abstract: According to one or more embodiments a computer-implemented method includes detecting, by a processor, an instruction to capture runtime data of a set of instructions in a computer program, the instruction detected based on a hook associated with the set of instructions. The method further includes determining whether the hook is an active expiring hook. The method further includes in response to the hook being an active expiring hook, determining a time condition associated with the expiring hook. The method further includes in response to the time condition being met, capturing the runtime data of the set of instructions in the computer program.

Abstract: According to one or more embodiments a computer-implemented method includes detecting, by a processor, an instruction to capture runtime data of a set of instructions in a computer program, the instruction detected based on a hook associated with the set of instructions. The method further includes determining whether the hook is an active expiring hook. The method further includes in response to the hook being an active expiring hook, determining a time condition associated with the expiring hook. The method further includes in response to the time condition being met, capturing the runtime data of the set of instructions in the computer program.