A system and method for developing an application. The system comprises a user interface, an object module for creating an object comprising an attribute and a function returning a value for the attribute, an activity module for defining an object's behavior, a simulation module comprising a rule governing the object's environment, and a build module, for compiling the application. The method comprises the steps of defining a plurality of objects and a relationship between the objects, extrapolating a logical clause based on the relationship, and building an application consistent with the logical clause.

Disclosed herein are methods, systems, and computer program products directed to a guidance engine. The guidance engine is configured to query a knowledge base for guidance with respect to a property of a software application. The guidance engine receives a responsive query from the knowledge base that is based on the property. The responsive query informs a user of the guidance engine how to address a vulnerability within the software application by performing a transform with respect to a property of the software application.

Some embodiments provide systems to determine contextual information comprising: an intent mining system that receive inquiry content that does not include personal identification information (PII) and is configured to determine an estimated intent information being sought by an intended recipient, identify a mapping to a sub-set of supplemental keywords corresponding to the intent information; and identify historic inquiries associated with actual historic product purchases relevant to the inquiry content and supplemental keywords, and obtain a listing of products associated with the inquiry content; a product association system that identifies a set of multiple products that each have a purchase threshold relationship with one or more products from the determined listing of products, and generate an enhanced listing of products; a topic extraction system that evaluates associations between product parameters of the enhanced listing of products to identify multiple associated topics and corresponding topic confidence scores.

Systems and methods are described for monitoring code execution within an on-demand code execution environment or other distributed code execution environment. The distributed, asynchronous nature of such environment can make determining the interactions between code executions difficult relative to traditional, non-distributed systems. The present disclosure enables the interrelations between code executions to be monitored by injecting monitoring information into the calls between those code executions. The monitoring information may be propagated through calls, such that a “path” or “trace” of code executions and calls can be determined. Data generated based on the monitoring information can be used to generate a profile for a set of code, so that a developer or other user may easily debug or optimize execution of the code.

Aspects of the disclosure relate to managing a set of warning data with respect to an execution phase in a computing environment. In embodiments, the computing environment may include a distributed computing environment or a stream computing environment. The set of warning data may be detected with respect to the execution phase. In embodiments, the set of warning data may be coupled with a computing artifact. In embodiments, the computing artifact may include a compilation which has a computing object in association with the set of warning data. Using the set of warning data, an execution action which pertains to the computing artifact may be determined. In embodiments, the execution action may include a code deployment to a set of computing units, a run-time check modification, or a process attribute modification. The execution action which pertains to the computing artifact may be performed.

A method, a system, and a computer program product for training a model for automatically evaluating a generated vulnerability remediation in a source code of an application are provided. The method includes the following steps. Training input data is obtained, where the training input data includes input features, and each of the input features includes a training vulnerability and a training remediation of the training vulnerability. Training output data is obtained, where the training output data includes output predictions, and each of the output predictions includes a training validation associated with the training vulnerability and the training remediation of the corresponding input feature. The model is trained based on the training input data and the training output data.

A compiler, IDE or other code analyzer may determine whether an instance variable declaration assignment is redundant. The code analyzer may also take action based on that determination. A code analyzer may be able to determine with certainty that a particular instance variable initialization or assignment is definitely redundant. The code analyzer may cause a compiler to automatically elide the redundant assignment from compiled source code. The code analyzer may be able to determine with certainty that a particular assignment is definitely not redundant. Additionally, a code analyzer may not be able to determine with certainty whether an instance variable assignment is definitely redundant or definitely not redundant. Additionally, the code analyzer may report a warning or other informative message indicating the redundancy property of the assignment, thus alerting the programming to a (possibly) redundant assignment.

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.

Methods and systems for program code conversion for cross-platform compatibility are disclosed. A plurality of original function calls are identified in an original program. The original program is targeted to a first platform, and the original function calls comprise invocations of a plurality of functions implemented on the first platform. It is determined whether individual ones of the original function calls correspond to alternative function calls in an index. For the original function calls that correspond to the alternative function calls in the index, the original function calls are replaced with corresponding alternative function calls in a modified program. The modified program is targeted to a second platform, and the alternative function calls comprise invocations of a plurality of functions implemented on the second platform.

A graph-based program specification includes components corresponding to tasks and directed links between ports of the components, including: a first type of link configuration defined by respective output and input ports of linked components, and a second type of link configuration defined by respective output and input ports of linked components. A compiler recognizes different types of link configurations and provides in a target program specification occurrences of a target primitive for executing a function for each occurrence of a data element flowing over a link of the second type. A computing node initiates execution of the target program specification, and determines at runtime, for components associated with the occurrences of the target primitive, an order in which instances of tasks corresponding to the components are to be invoked, and/or a computing node on which instances of tasks corresponding to the components are to be executed.