A system for conducting user interface (UI) software component testing has been developed. The test of the UI software component is initiated and a display of the test results are shown to a user in a browser used in the testing. A code editor used for debugging by the user receives the test results from the browser and presents the test results to the user for debugging. The browser webview and the code editor are simultaneously displayed to the user in a dual screencast window during the UI software testing.

A representation of a category of task offloaders is stored, in response to receiving a descriptor of the category, in a database of categories of offloaders which can be attached to servers of one or more classes. An indication of server configurations which include a task offloader of the category is provided via programmatic interfaces. A task is executed at a task offloader of a server with one of the server configurations.

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.

Disclosed herein is technology to capture and restore a state of a development environment. An example method may include: determining, by a processing device, a state of a first development environment, wherein the first development environment displays content of a set of files that correspond to a program modification; storing state data that represents the state of the first development environment, wherein the state data identifies the files in the set; receiving a request to update a second development environment; and updating, using the state data, a state of the second development environment, wherein the updated state of the second development environment displays the content of the set of files corresponding to the program modification.

Embodiments of the present disclosure provide a wireless debugger and a wireless debugging system. The wireless debugger includes: a processor, a wireless communication module, and a first peripheral interface; the processor is electrically connected to the wireless communication module and the first peripheral interface, respectively; the processor, is configured to receive debugging instructions through the wireless communication module, and the debugging instructions are used to instruct debugging/stop debugging a target board; the processor, is further configured to parse the debugging instructions and convert the parsed debugging instructions so that the debugging instructions are adapted to a protocol of the first peripheral interface; and the processor, is further configured to transmit the converted debugging instructions to the to-be-debugged target board through the first peripheral interface. Debugging control is convenient and reliable.

An automated program repair tool utilizes a neural transformer model with attention to predict the contents of a bug repair in the context of source code having a bug of an identified bug type. The neural transformer model is trained on a large unsupervised corpus of source code using a span-masking denoising optimization objective, and fine-tuned on a large supervised dataset of triplets containing a bug-type annotation, software bug, and repair. The bug-type annotation is derived from an interprocedural static code analyzer. A bug type edit centroid is computed for each bug type and used in the inference decoding phase to generate the bug repair.

A method of debugging an applet, an electronic device, a storage medium. The method includes: selecting at least one debugging module from a plurality of debugging modules as a target debugging module according to a first debugging instruction; running the applet so that the target debugging module is interrupted in response to running to the target debugging module in the applet; and awakening the interrupted target debugging module for debugging according to a second debugging instruction, so as to generate a debugging result.

A method for analyzing software with pointer analysis may include obtaining a software program, and determining a first independent program slice of the software program describing a first code segment of the software program. The method may further include determining, using a first pointer analysis objective, a first result from performing a first pointer analysis on the first independent program slice, and determining, using the first result, a first dependent program slice of the software program. The method may further include determining, using a second pointer analysis objective, a second result from performing a second pointer analysis on the first dependent program slice. The method may further include generating a report, using these results, indicating whether the software program satisfies a predetermined criterion.

An effective system for verifying safety of an artificial intelligence system includes a feature quantity information accepting unit which accepts feature quantity information that includes values of plural feature quantities, that are assumed as those used in an artificial intelligence system, in each of plural first test data used for a test for verifying safety of the artificial intelligence system; and a judgment unit which judges a first combination, that is a combination that is not included in the plural first test data, in combinations of values that plural feature quantities may take, or a second combination, with it plural correct analysis results that should be derived by the artificial intelligence are associated, in the combinations of the values that the plural feature quantities may take.

A computing device including a processor configured to receive source code including a plurality of source code modules. The processor may generate executable code from the source code and assign two or more reload indicators to two or more executable code modules. The processor may execute the executable code. During execution of the executable code, the processor may receive a source code update and generate an executable code update from the source code and the source code update. The processor may apply the executable code update to the executable code to generate updated executable code. The processor may generate a reload ordering of two or more reload operations corresponding to the reload indicators. As specified by the reload ordering, the processor may perform the two or more reload operations at the two or more respective executable code modules. The processor may execute the updated executable code.