Disclosed herein are techniques for visualizing and configuring controller function sequences. Techniques include identifying at least one executable code segment associated with a controller; analyzing the at least one executable code segment to determine at least one function and at least one functional relationship associated with the at least one code segment; constructing, a software functionality line-of-code behavior and relation model visually depicting the determined at least one function and at least one functional relationship; displaying the software functionality line-of-code behavior and relation model at a user interface; receiving a first input at the interface; in response to the received first input, animating the line-of-code behavior and relation model to visually depict execution of the at least one executable code segment on the controller; receiving a second input at the interface; and in response to the received second input, animating an update to the line-of-code behavior and relation model.

The disclosure relate to detecting performance regressions in software used to control autonomous vehicles. For instance, a simulation may be run using a first version of the software. While the simulation is running, CPU and memory usage by one or more functions of the first version of the software may be sampled. The sampled CPU and memory usage may be compared to CPU or memory usage by each of the one or more functions in a plurality of simulations each running a corresponding second version of the software. Based on the comparisons, an anomaly corresponding to a performance regression in the first version of the software relating to one of the one or more functions may be identified. In response to detecting the anomaly, the first version of the software and the one of the one or more functions may be flagged for review.

A first storage controller includes a first input and output controller performs input and output processing on host data, and a first management controller. A second storage controller includes a second input and output controller performs input and output processing on host data, and a second management controller. The first management controller is configured to verify software to be executed by the first management controller and software to be executed by the first input and output controller. The second management controller is configured to verify software to be executed by the second management controller and software to be executed by the second input and output controller. The first management controller is configured to verify the software to be executed by the second input and output controller in place of the second management controller when a failure is detected from the second management controller.

A non-transitory computer-readable recording medium stores a program that causes a computer to execute a process, the process includes acquiring a first log using a first program that monitors a first function for securing resources managed by an operating system (OS), the first log including information that allows for identification of a resource secured by software and a time point when the software secures the secured resource, acquiring a second log using a second program that monitors a second function for releasing the resources managed by the OS, the second log including information that allows for identification of a resource released by the software and a time point when the software releases the released resource, and outputting a first time point when the software secures a target resource in association with a second time point when the target resource is released, based on the first log and the second log.

Technologies are provided for assigning developers to source code issues using machine learning. A machine learning model can be generated based on multiple versions of source code objects (such as source code files, classes, modules, packages, etc.), such as those that are managed by a version control system. The versions of the source code objects can reflect changes that are made to the source code objects over time. Associations between developers and source code object versions can be analyzed and used to train the machine learning model. Patterns of similar changes to various source code objects can be detected and can also be used to train the machine learning model. When an issue is detected in a version of a source code object, the model can be used to identify a developer to assign to the issue. Feedback data regarding the developer assignment can be used to re-train the model.

A source code correction assistance apparatus is configured to include a storage device that stores an updated source code, and an arithmetic operational device that generates, as an evaluation code template of the updated source code, a template including a conditional branch sentence related to each case of success or failure of an input condition, notifies an evaluator terminal of a request to create an evaluation code based on the template, controls an access to the updated source code by the evaluator, receives editing by the evaluator on the conditional branch sentence in the template, generates a list of input values for executing all control paths of the evaluation code after the editing, and generates an evaluation code driver that automatically executes the evaluation code by inputting the input value.

During a software development lifecycle of a software application, application code is modified and multiple versions are built and packaged to be installed on different computing systems, such as on a software development computing system, a software testing computing systems, and/or production or end-user computing systems. A runtime error optimization engine analyzes, using a first artificial intelligence model, a build package to predict whether it may encounter runtime errors causing an installation to fail. When an error is identified, a runtime error orchestration engine may utilize a second artificial intelligence model to identify a solution, where the runtime error orchestration engine rebuilds the build package based on an identified solution and initiates installation via a deployment pipeline.

In computer-implemented method, computer system, and/or computer program product, a processor(s) obtains a test (of steps(s)) to verify program code for deployment in distributed computing system. The processor(s) determines pre-defined operations correlating to the step(s). The processor(s) automatically distributes the pre-defined operations to a resources of a distributed computing system, for execution. The processor(s) monitors the execution and saves at least one screenshot as each step. The processor(s) generates a user interface with a status indicator. The processor(s) continuously update the user interface, based on the monitoring, to reflect a progression of the portion of the one or more resources through the step(s).

A system for enforcing compliance and testing for software development, comprising an indexing service configured to create a dataset by processing and indexing source code of a project by a developer, perform a code audit on the indexed source code, store results from the code audit in the dataset, gather additional information relating to the provided project, store the additional information in the dataset, and store the dataset into memory; and a monitoring service configured to continuously monitor the project for source code changes and make changes to the dataset as needed. Further comprising an enforcement module to automatically verify code and other media related to the software development process by ensuring obligations from a rules database are met and where not able to automate the compliance check forward to an appropriate authority, receive back the manually reviewed compliance check, then produce and implement automated recommendations for compliance adherence.

A system and method for meta-indexing, search, compliance, and test framework for software development using smart contracts is provided, comprising an indexing service configured to create a dataset by processing and indexing source code of a project provided by a developer, perform a code audit on the indexed source code, store results from the code audit in the dataset, gather additional information relating to the provided project, store the additional information in the dataset, and store the dataset into memory; and a monitoring service configured to continuously monitor the project for at least source code changes and make changes to the dataset as needed. Additionally, a smart contract authority creates and enforces smart contracts for every transaction taking place upon the software essentially mandating and guaranteeing the security and authenticity of the software during the software's development and use.