Abstract:
An analysis system may perform network analysis on data gathered from an executing application. The analysis system may identify relationships between code elements and use tracer data to quantify and classify various code elements. In some cases, the analysis system may operate with only data gathered while tracing an application, while other cases may combine static analysis data with tracing data. The network analysis may identify groups of related code elements through cluster analysis, as well as identify bottlenecks from one to many and many to one relationships. The analysis system may generate visualizations showing the interconnections or relationships within the executing code, along with highlighted elements that may be limiting performance.
Abstract:
An analysis system may perform network analysis on data gathered from an executing application. The analysis system may identify relationships between code elements and use tracer data to quantify and classify various code elements. In some cases, the analysis system may operate with only data gathered while tracing an application, while other cases may combine static analysis data with tracing data. The network analysis may identify groups of related code elements through cluster analysis, as well as identify bottlenecks from one to many and many to one relationships. The analysis system may generate visualizations showing the interconnections or relationships within the executing code, along with highlighted elements that may be limiting performance.
Abstract:
The purity of a function may be determined after examining the performance history of a function and analyzing the conditions under which the function behaves as pure. In some cases, a function may be classified as pure when any side effects are de minimis or are otherwise considered trivial. A control flow graph may also be traversed to identify conditions in which a side effect may occur as well as to classify the side effects as trivial or non-trivial. The function purity may be used to identify functions for memoization. In some embodiments, the purity analysis may be performed by a remote server and communicated to a client device, where the client device may memoize the function.
Abstract:
Recommendations may be generated while calculating performance metrics from multiple uses of a software component. A tracing service may collect trace data from multiple uses of a software component, where each use may be done on different conditions. The performance metric analysis may identify various factors that may affect the performance of a software component, then present those factors to a user in different delivery mechanisms. In one such mechanism, a recommended set of hardware and software configurations may be generated as part of an operational analysis of a software component.
Abstract:
An operating system may be configured using a control flow graph that defines relationships between each executable module. The operating system may be configured by analyzing an application and identifying the operating system modules called from the application, then building a control flow graph for the configuration. The operating system may be deployed to a server or other computer containing only those components identified in the control flow graph. Such a lightweight deployment may be used on a large scale for datacenter servers as well as for small scale deployments on sensors and other devices with little processing power.
Abstract:
A behavior model for a software application may identify a set of execution sequences that begin from a set of origins. The sequences may be further defined by a set of exits. In some cases, the sequences may be decomposed into subsequences or n-grams. The execution sequences and their frequencies may define a usage or behavior model for the application. The sequences may be defined by semantic level operations of an application, which may be defined by functions, call backs, API calls, or other blocks of code execution. The behavior model may be used for determining code coverage, comparing versions of applications, and other uses.
Abstract:
A load balanced system may incorporate instrumented systems within a group of managed devices and distribute workload among the devices to meet both load balancing and data collection. A workload distributor may communicate with and configure several managed devices, some of which may have instrumentation that may collect trace data for workload run on those devices. Authentication may be performed between the managed devices and the workload distributor to verify that the managed devices are able to receive the workloads and to verify the workloads prior to execution. The workload distributor may increase or decrease the amount of instrumentation in relation to the workload experienced at any given time.
Abstract:
An execution environment for functional code may treat application segments as individual programs for memory management. A larger program of application may be segmented into functional blocks that receive an input and return a value, but operate without changing state of other memory objects. The program segments may have memory pages allocated to the segments by the operating system as other full programs, and may deallocate memory pages when the segments finish operating. Functional programming languages and imperative programming languages may define program segments explicitly or implicitly, and the program segments may be identified at compile time or runtime.
Abstract:
Error logs, bug reports, and other databases identifying problems with a tracer system may be mined to determine how a tracer may interact with a given function, module, or other group of functions. Based on such reports, a tracer may be configured to avoid certain functions or to trace such functions in a specific manner. In some cases, tracer may be configured to limit tracing to certain parameters or with other limitations to avoid any known conditions under which errors occur.
Abstract:
A tracing system may be updated to include, exclude, or modify tracing configurations for functions based on how a user consumes tracing results. The user's interactions with graphical representations, inspections of data, and other interactions may indicate which functions may be interesting and which functions may not be. The user's interactions may be classified by use, such as during debugging, performance testing, and ongoing monitoring, and multiple user's interactions with the same function, library, module, source code file, or other groups of functions may be combined to predict a user's interest in a function.