Systems and methods provide for an integrated script development and script validation platform. The integrated script development and script validation platform archives data in a way such that the dependencies between contributions of code strings (e.g., script sets) are detected and recorded. That is, the systems and methods detect dependency branches in the script code of script sets. By doing so, the systems and methods may identify individual performance characteristics for a given script set as well as determine the overall impact on the application itself.

An instrumentation analysis system processes data streams by executing instructions specified using a data stream language program. The data stream language allows users to specify a search condition using a find block for identifying the set of data streams processed by the data stream language program. The set of identified data streams may change dynamically. The data stream language allows users to group data streams into sets of data streams based on distinct values of one or more metadata attributes associated with the input data streams. The data stream language allows users to specify a threshold block for determining whether data values of input data streams are outside boundaries specified using low/high thresholds. The elements of the set of data streams input to the threshold block can dynamically change. The low/high threshold values can be specified as data streams and can dynamically change.

An information processing apparatus includes a memory; and a processor coupled to the memory and the processor configured to: generate first information including configuration information and/or interface information of a first program of an old version based on code of the first program of the old version, and generate second information including configuration information and/or interface information of the first program of a new version based on code of the first program of the new version; and generate information on compatibility between the first program of the old version and the first program of the new version based on whether there is a difference between the first information and the second information generated by the processor.

A taint report represents a taint flow from a source value at a source program point to a sink value at a sink program point. Candidate watchpoints that correspond to taint-like values similar to the source value may be inferred from an execution trace. Different subsets of candidate watchpoints represent solutions to the problem of determining an optimal subset of watchpoints contributing to a taint flow. Using a hill-climbing heuristic, incremental improvements are efficiently applied to a solution until no more improvements are found. An objective function may determine whether one solution improves another solution. The objective function may be based on validity, understandability, and performance. Validity favors candidate watchpoints that reduce the edit distance between the source and sink values. Understandability favors candidate watchpoints included in a call chain from the source program point to the sink program point. Performance favors small subsets of candidate watchpoints.

Disclosed herein are techniques for analyzing control-flow integrity based on functional line-of-code behavior and relation models. Techniques include receiving data based on runtime operations of a controller; constructing a line-of-code behavior and relation model representing execution of functions on the controller based on the received data; constructing, based on the line-of-code behavioral and relation model, a dynamic control flow integrity model configured for the controller to enforce in real-time; and deploying the dynamic control flow integrity model to the controller.

A system and method for real-time or near real-time anomaly detection and root cause automation in production environments or in other environments using shrunk dynamic call graphs are provided. The system includes an instrumentation agent that generates shrunk dynamic call graphs and exceptions/errors by injecting monitoring code or probes or call-tags into monitored application, a data agent that forwards collected data to the analysis engine over a network, an analysis engine that performs continuous clustering using machine learning, anomaly, and root cause detection. The system also includes a reporting module to report the anomaly.

Methods, systems, and computer-readable media for automated deprecation analysis in a service-oriented system are disclosed. A service deprecation system determines that a first service in a service-oriented system calls a second service in the service-oriented system. The service deprecation system determines that the second service calls a third service in the service-oriented system. The service deprecation system determines that logic of the second service is duplicated by logic of the first service. The first service is modified to call the third service instead of the second service, and the second service is disabled or removed from the service-oriented system.

An instrumentation analysis system processes data streams by executing instructions specified using a data stream language program. The data stream language allows users to specify a search condition using a find block for identifying the set of data streams processed by the data stream language program. The set of identified data streams may change dynamically. The data stream language allows users to group data streams into sets of data streams based on distinct values of one or more metadata attributes associated with the input data streams. The data stream language allows users to specify a threshold block for determining whether data values of input data streams are outside boundaries specified using low/high thresholds. The elements of the set of data streams input to the threshold block can dynamically change. The low/high threshold values can be specified as data streams and can dynamically change.

A device may receive software data identifying current logs and events associated with software products utilized by an entity and may process the software data, with a machine learning model, to generate error severity scores for the software products. The machine learning model may be trained based on historical software data identifying events and logs associated with software products utilized by the entity and based on a combination of historical health scores, historical sentiment scores, and historical dissimilarity scores for the software products. The device may process the error severity scores, with a prioritization model, to generate prioritized error scores and may process the error severity scores and the prioritized error scores, with a root cause analysis model, to generate root cause data identifying root causes associated with the error severity scores. The device may perform one or more actions based on the root cause data.

Systems and methods for maintaining the safety of a software-based system. One method includes automatically generating a first artifact tree for a hazard for a first version of the system and automatically transforming the first artifact tree into a first augmented tree using a set of heuristics. The method also includes automatically generating a second artifact tree for the hazard for a second version of the system and automatically transforming the second artifact tree for the hazard into a second augmented tree using the set of heuristics. The method further includes automatically comparing the first augmented tree and the second augmented tree to generate a delta view, and automatically generating, based on the delta view, at least one selected from a group consisting of a safety warning for the second version of the software-based system and an actionable recommendation to maintain safety of the second version of the software-based system.