Aspects of the present disclosure involve systems, methods, devices, and the like for creating an application lifecycle management platform for big data applications. In one embodiment the lifecycle management platform can include a multiple-layer container file that integrates multiple big-data tools/platforms. The system may create a generic template application, create a build environment for the generic template application, create a test environment for the generic template application, and run the built generic template application in the test environment prior to the user writing any new code in the generic template application. In one embodiment, the test environment includes a container management system or virtual machine that launches the big data application (which may be the generic template application before a developer edits the file) on a separate big-data server cluster.

A system, product and process for building a package dependencies data structure. The method comprises determining a package instance identifier of the package to be processed. The package instance identifier is determined based on a unique identifier of the package to be processed and based on a unique identifier of a dependency sub-tree of the package to be processed in the package instance identifier. The package instance identifier is utilized to determine if a node representing the package to be processed exists or not. In case the node does not exist already in the data structure, the node is created and added.

This disclosure relates generally to the field of source code processing, and, more particularly to a method and system for identification of redundant function-level slicing calls. The method disclosed generates program dependence graphs (PDGs) based on a slicing criteria and a function corresponding to the function-level slicing call. Further the method classifies the function-level slicing call into redundant or non-redundant by traversing the PDGs and checking if a predefined condition is satisfied or not. The function-level slicing calls are classified as redundant if the check is not satisfied and are classified as non-redundant if the check is satisfied. The disclosed method can be used in identifying redundant function-level slicing calls in applications such as automated false positive elimination (AFPE), automated test case generation and so on.

A method for receiving a first portion of object code, analyzing a first portion of object code in a static manner to determine a call tree hierarchy, dividing, by a synthetic compiler, the first portion of object code into a plurality of modules; and starting to run the first portion of object code to start a runtime phase, with the running of the first portion of the object code including: (i) lazy loading of the modules of the plurality of modules of the first portion of object code, and/or (ii) eager unloading of the modules of the plurality of modules of the first portion of object code.

A method for mitigating errors in the transportation of configuration data may include identifying, at a development system, dependent configuration data associated with a first transport request. The dependent configuration data may implement a customization to a software application hosted at a production system. A reference table identifying the dependent configuration data may be sent to the production system. A missing object list identifying dependent configuration data absent from the production system may be generated at the production system based on the reference table. The missing object list may be sent to the development system where a corrective action may be performed such that the dependent configuration data identified by the missing object list as being absent from the production system is sent to the production system in the first transport request and/or a second transport request. Related systems and articles of manufacture, including computer program products, are also provided.

Techniques are described for enabling an application modernization system to efficiently identify various types of software development “anti-patterns” (including cloud anti-patterns) associated with software applications. An anti-pattern is broadly any characteristic of a software application or system that represents an undesirable or suboptimal solution to a problem within a given software design context. Depending on the particular software design context, examples of possible anti-patterns and cloud anti-patterns can include the use of insecure network protocols, an application's reliance on local file system access, the use of a deprecated or outdated class, function, or library, etc. An application modernization system described herein includes a modernization assessment client application that identifies the presence of anti-patterns in users' software applications using a collection of anti-pattern definitions (e.g., defined based on an anti-pattern definition syntax) obtained from an application modernization service of a cloud provider network.

A method and apparatus for automatically deploying artifacts are disclosed. In one embodiment, the method comprises generating a trusted configuration artifact with a forward immutable continuous integration (CI) implemented as a build pipeline; accessing, by an orchestration system, the trusted configuration artifact from the IAC repository; and automatically deploying the configuration to change a state of the cluster, according to an orchestration type associated with the trusted configuration artifact, including determining the orchestration type.

Performing usage-based software library decomposition is disclosed herein. In some examples, a processor device generates a first library graph representing a first software library including multiple functions. The first library graph comprises a plurality of nodes that each correspond to a function of the first software library. The processor device identifies a function within the first software library (“invoked function”) that is directly invoked by an application that depends on the first software library, then generates a call graph including nodes within the first library graph (“dependency nodes”) corresponding to either the invoked function or another function invoked by the invoked function during application execution. Using the call graph, the processor device generates a second software library including only functions of the first software library corresponding to dependency nodes of the call graph.

Dataflow optimization by dead store elimination focusing on logically dividing a contiguous storage area into different portions by use to allow a different number and type of dataflow and dead store techniques on each portion. A first storage portion, containing the storage for control flow related metadata, is split from a remaining storage portion. Liveness analysis is executed on the first storage portion using bitvectors with each bit representing four bytes. The remaining storage portion, containing the temporary storage for computational values, is processed using a deadness-range-based dataflow analysis. IN and OUT sets for each basic block are generated by processing blocks GEN and KILL sets by performing a backwards intersection dataflow analysis. Stores that write to the set of dead ranges in the IN sets of blocks are eliminated as dead stores.

Techniques for generating a dataflow graph include generating a first dataflow graph with a plurality of first nodes representing first computer operations in processing data, with at least one of the first computer operations being a declarative operation that specifies one or more characteristics of one or more results of processing of data, and transforming the first dataflow graph into a second dataflow graph for processing data in accordance with the first computer operations, the second dataflow graph including a plurality of second nodes representing second computer operations, with at least one of the second nodes representing one or more imperative operations that implement the logic specified by the declarative operation, where the one or more imperative operations are unrepresented by the first nodes in the first dataflow graph.