The present application is directed towards systems and methods for cluster-based code analysis and transformation. Cluster-based analysis may group code objects based on their similarity across functional areas, such as where a code object is cloned in multiple areas (e.g. sort functions that are duplicated across areas, or reports or tables that are identical). In some implementations, objects may be grouped into clusters by type, or based on reading from or writing to a common table. In some implementations, clustering at different layers may be possible.

A computing device for receiving a design problem statement describing a design problem in a controlled natural language (CNL) that defines permitted lexicons and syntax structures. The design problem statement is processed using the CNL lexicons and syntax structures to produce a job description executable by a design application for generating a design solution for the design problem statement. An improved CNL user interface that assists users to produce valid design problem statements that are CNL-compliant. The CNL user interface receives user-selectable terms that are compliant with the CNL lexicons and generates candidate problem statements that are compliant with CNL syntax structures and receives a selection of a candidate problem statement that is added to the design problem statement. A graphical user interface may display a graphical representation of a design problem statement that can be directly modified. A dialogue-based design process to explore possible design intentions and design solutions.

Predicting physical memory attributes by compiler analysis of code blocks includes receiving source code including at least one code block, and identifying a buffer associated with the at least one code block. Buffer access characteristics associated with the buffer are determined from the at least one code block. The buffer access characteristics are mapped to physical memory attributes associated with one or more physical memories of a computing system. Executable program code including a system call associated with memory allocation is generated based upon the physical memory attribute values.

Embodiments include a method, a cognitive software porting assistant system, and a computer program product for operating a cognitive software porting assistant system. Embodiments include analyzing code to identify strings of the code, tokenizing the strings of the code into tokens, and identifying a first set of tokens and a second set of tokens from the tokens, wherein the first set of tokens corresponds to a first platform and the second set of tokens corresponds to the second platform. Embodiments also include generating a first syntax tree for the first set of tokens and a second syntax tree for the second set of tokens, and serializing the first syntax tree and the second syntax tree. Embodiments include comparing the first serialized syntax tree with the second serialized syntax tree, and saving a mapping of the first serialized syntax tree and second serialized syntax tree responsive to the comparison.

A protocol model generator according to the present disclosure includes: a memory in which a protocol model generating program is stored; and a processor configured to execute the program. Herein, upon execution of the program, the processor extracts multiple strings from a binary corresponding to a protocol, generates a message pool including multiple candidate messages to be used in the protocol corresponding to the binary on the basis of the extracted multiple strings, and generates a protocol model corresponding to the protocol and configured to include nodes corresponding to the respective candidate messages included in the message pool. Further, the candidate messages include messages generated on the basis of the strings and response messages generated corresponding to the messages, and the protocol model is generated including one or more nodes and transition information between the nodes.

Predicting physical memory attributes by compiler analysis of code blocks includes receiving source code including at least one code block, and identifying a buffer associated with the at least one code block. Buffer access characteristics associated with the buffer are determined from the at least one code block. The buffer access characteristics are mapped to physical memory attributes associated with one or more physical memories of a computing system. Executable program code including a system call associated with memory allocation is generated based upon the physical memory attribute values.

Embodiments include a method, a cognitive software porting assistant system, and a computer program product for operating a cognitive software porting assistant system. Embodiments include analyzing code to identify strings of the code, tokenizing the strings of the code into tokens, and identifying a first set of tokens and a second set of tokens from the tokens, wherein the first set of tokens corresponds to a first platform and the second set of tokens corresponds to the second platform. Embodiments also include generating a first syntax tree for the first set of tokens and a second syntax tree for the second set of tokens, and serializing the first syntax tree and the second syntax tree. Embodiments include comparing the first serialized syntax tree with the second serialized syntax tree, and saving a mapping of the first serialized syntax tree and second serialized syntax tree responsive to the comparison.

A computer readable storage medium, system and method for improving automated testing systems to include a first and second behavioral data. The first behavioral data is collected periodically and the second behavioral data is collected in real time. The receipt of the first behavioral data and a second behavioral data are followed by the receipt of a system configuration template. A test case is updated based on the first and second behavioral data, and an automated test environment is reconfigured based on the first behavioral data, second behavioral data, and the system configuration template. The test executes in the automated test environment producing a test result.

A method to process subroutine-structured graph-based intermediate representations during formal language processing implemented by a computing device. The method includes classifying a set of subroutines identified in an intermediate representation of code according to mutually recursive relationships between subroutines in the set of subroutines, recording the mutually recursive relationships, labeling to track the mutually recursive relationships, constructing a set of graph representations, collecting partial positions that distinguish points of action in generated code, labeling nodes of the graph of the intermediate representation, generating a subsequent intermediate representation by serialization of the graph of the intermediate representation through pre-order depth-first traversal, and creating the generated code from the intermediate representation.

The present application is directed towards systems and methods for cluster-based code analysis and transformation. Cluster-based analysis may group code objects based on their similarity across functional areas, such as where a code object is cloned in multiple areas (e.g. sort functions that are duplicated across areas, or reports or tables that are identical). In some implementations, objects may be grouped into clusters by type, or based on reading from or writing to a common table. In some implementations, clustering at different layers may be possible.