A method for computer program source level trust assurance in an Internet of Things (IoT) device includes receiving a request to install a computer program in an IoT device and storing, in temporary memory, a file containing a binary form of the computer program and an authenticity fingerprint incorporated as part of the binary form of the computer program. The method additionally includes extracting the authenticity fingerprint from the binary form of the computer program, retrieving a different fingerprint from remote storage and comparing the different fingerprint to the extracted fingerprint. Finally, the method includes installing the computer program into the IoT device in response to the comparison indicating that the extracted fingerprint is identical to the different fingerprint, but otherwise rejecting the request to install the computer program.

The ability to comprehend the context of a given programming artifact and extracting the underlying functionality is a complex task extending beyond just syntactic and semantic analysis of code. All existing automation capabilities, hence heavily depend on manual involvement of domain experts. Even recent approaches leveraging Machine Learning Capabilities are supervised techniques, whereby the dependency on domain experts still remains—in preparing suitable training sets. A method and system for automated classification of variables using unsupervised distribution agnostic clustering has been provided. The present disclosure focuses to tap the flexibility of the code and presents a domain agnostic approach using unsupervised machine learning which automatically extracts the context from source code, by classifying the underlying elements of the code. The method and system do not require any manual intervention and opens a wide range of opportunities in reverse engineering and variable level analysis space.

Conventional methods for application development are rigid, complex, costly and ineffective in implementing new and changing technologies. The embodiments herein provide a model driven system and method for development of micro service applications. A set of user interface components and one or more domain components for a plurality of micro services designs of a plurality of applications pertaining to one or more technologies are modeled and validated. Further, a plurality of codes are generated according to the one or more modeled domain components of the plurality of micro services designs of the plurality of applications satisfying one or more requirements. The plurality of generated codes are further customized and validated to ensure design and code consistency. The plurality of customized codes are further containerized to provide a plurality of containerized applications which are deployed into a target deployment environment for execution using a plurality of configuration files.

A system and a method for application transformation to cloud by conversion of an application source code to a cloud native code is provided. A first and a second transformation recommendation path is received and a set of remediation templates are applied based on the first and the second transformation recommendation paths where the set of remediation steps comprises pre-defined parameterized actions. The system comprises a microservices unit configured to optimize assessment and implementation of microservices code for multiple target cloud platforms by determining a count of microservices anti-patterns in a microservices code, wherein the anti-patterns represent a pattern of the microservices code and ascertaining a current state of the microservices code by determining a maturity score. A set of repeatable steps associated with microservices code development are provided in a bundled form for accelerated implementation of changes in the microservices code for deployment on the multiple target cloud platforms.

A system comprises at least one hardware processor and a memory storing instructions. When executed, the instructions cause the at least one hardware processor to perform operations comprising receiving, in a computing process, a Java user-defined table function (Java UDTF), the Java UDTF including code related to a process method to be performed that includes receiving one or more input tables and transforming the one or more input tables to an output table; determining, using at least a security policy, whether performing one or more portions of the process method are permitted; and performing portions of the process method determined to be permitted.

A computing device is provided, including a processor configured to receive source code at a compiler. The source code may include at least one compound conditional having a plurality of conditions. For each condition of the plurality of conditions, the source code may further include a respective code block including instructions to evaluate the condition. For each ordering of a plurality of orderings of the plurality of conditions, the processor may determine that the ordering satisfies one or more legality constraints. For each ordering of the plurality of orderings that satisfy the one or more legality constraints, the processor may determine a respective estimated computational cost for that ordering. The processor may reorder the plurality of conditions to have an ordering that has a lowest estimated computational cost of the plurality of orderings that satisfy the one or more legality constraints.

A computer-implemented method of monitoring programmatic containers (containers) through executing a computer program in a kernel space is disclosed. The method comprises storing trace data in a memory buffer that is shared by the kernel space and a user space, the trace data being related to execution of a process associated with a container at an execution point of the process. The method also comprises retrieving container data related to the container through raw access of one or more kernel data structures when execution of the process is stopped. In addition, the method comprises storing the container data in association with the trace data in the memory buffer.

One embodiment of the present invention provides an information processing apparatus which detects lack of information set to an information model and recommends editing. An information processing apparatus which is one embodiment of the present invention includes an acquirer and an editing recommender. The acquirer acquires content to be edited for the information model. The editing recommender outputs information which recommends editing the information model on the basis of a function template to which information set to the information model is applicable or a function model to which the information set to the information model is applied, and the content to be edited.

Methods and computer-readable media are disclosed herein for generating asynchronous runtime compatible applications from non-asynchronous applications. In embodiments, source code for the application that is not compatible with asynchronous processing is examined. The source code is parsed in order to identify unsafe functions that will cause failures of the application when processed in an asynchronous runtime. The source code corresponding to those unsafe functions is modified by adding asynchronous functions and commands to the source code and restructuring the source code. The modified source code may then be provided to an asynchronous runtime environment as the application is now compatible with asynchronous processing.

Binaries configured for execution within respective computing environments may be disassembled into architecture-specific intermediate (AIL) representations. The AIL representations may be converted into canonical intermediate language (CIL) representations. The CIL representations may comprise normalized, architecture-independent code configured to characterize functionality of respective components of a binary (e.g., respective functions or the like). Feature vectors may be extracted from the CIL representations. The feature vectors may be used to identify components of respective binaries, assign security classifications to the binaries, and/or the like.