A system is provided for computer code development environment cloning and authentication using a distributed server network and machine learning. In particular, the system may use a machine learning algorithm configured to automatically identify and analyze changes in computing code between two or more environments and publish a record of said changes to a private distributed register stored on a plurality of distributed server nodes. Based on the analysis, the system may generate one or more recommended changes to the source code. If the changes are confirmed by one or more authorized users, the system may automatically implement the changes and publish a confirmation record of the implemented changes to the distributed register. In this way, the system may provide an efficient way to ensure synchronization of code across multiple computing environments.

A repository of graph based visual programming language code instances is analyzed. A similar code portion pattern duplicated is detected among a group of graph based visual programming language code instances included in the repository of graph based visual programming language code instances including by using an index and tokenizing one or more graph nodes connected by one or more graph edges included in a flow corresponding to at least one graph based visual programming language code instance in the group of graph based visual programming language code instances. Within a visual representation of at least one of the group of graph based visual programming language code instances, elements belonging to the detected similar code portion pattern are visually indicated.

Code search is used to support various features. Code segments may be indexed using a code structure representation of the code segment. Code segments may be compared for similarity to identify a match with an input code segment using a comparison of logic trees generated for the input code segment and a stored code segment in an entry with a matching index value.

A computer-implemented method for checking the integrity of a target computer program to be executed in a computer system.

A computer implemented method includes obtaining an original graph data structure including multiple stored nodes connected by multiple edges. The stored nodes include multiple operation stored nodes and multiple data stored nodes. The method further includes generating an auxiliary graph data structure from the original graph data structure. The auxiliary graph data structure includes the operation stored nodes. The method further includes executing a pattern mining tool on the auxiliary graph data structure to obtain a pattern list, traversing the auxiliary graph data structure to identify multiple instances of identified patterns in the pattern list, and presenting the instances.

A computer-implemented method for determining an origin of a target source code for a computer program or a part thereof. The method involves: Searching a plurality of software archives from different sources in a global computer network to find occurrences of the target source code among code files in said software archives. For every found occurrence of the target source code, the method further involves collecting key information about the matching source code files and, from the key information collected a frequency map is built that contains, for each keyword found in the key information, a keyword count value being indicative of the number of times the keyword occurs in the key information. The method may further involves applying a scoring scheme to the matching source code files based on the built frequency map (310), determining a highest score (Vmax) among the matching source code files after the scoring scheme has been applied, and determining the origin of the target source code as the matching source code file having the highest score.

Techniques are provided for provenance-based software script reuse. One method comprises extracting provenance data from source code including, for example, source code fragments, wherein the extracted provenance data indicates a control flow and a data flow of the source code; encapsulating source code fragments from the source code that satisfy one or more similarity criteria as a reusable source code fragment; and providing a repository of encapsulated reusable source code fragments for reuse during a development of new software scripts. The repository of encapsulated reusable source code fragments optionally comprises a searchable database further including, for example, the provenance data, data annotations, input parameters and generated results for the corresponding source code fragment.

A data mining technique is used to find large frequently-occurring source code patterns from methods/APIs that can be used in code development. Simplified trees that represent the syntactic structure and type and method usage of a source code fragment, such as a method, are mined to find closed and maximal frequent subtrees which represent the largest frequently-occurring source code patterns or idioms associated with a particular type and method usage. These idioms are then used in an idiom web service and/or a code completion system to assist users in the development of source code programs.

In various embodiments, a process for detecting duplicated code patterns in visual programming language code instances includes analyzing a repository of graph based visual programming language code instances and detecting a similar code portion pattern duplicated among a group of graph based visual programming language code instances included in the repository of graph based visual programming language code instances including by using an index and tokenizing a flow corresponding to at least one graph based visual programming language code instance in the group of graph based visual programming language code instance. The process includes visually indicating elements belonging to the detected similar code portion pattern within a visual representation of at least one of the group of graph based visual programming language code instances.

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.