Amazon Technologies, Inc.

Mihaela Petrescu

Carlos Alejandro Arguelles

Adithya Venkatesh

Vijay Pratap Singh

Justin Alexander Tisi

Yan Peng

James Beavis

Kiran Chitluri

Choi Yong Ngo

Andy Kohn

Peter Ramensky

Xin-Liu Yao

Sahar Zeineddine

Nikhil Ravishankar

Choi Young Ngo

Methods, systems, and computer-readable media for analysis of code coverage differences across environments are disclosed. A code coverage profiling system determines a first code coverage profile associated with execution of program code in a first environment. The first code coverage profile indicates one or more portions of the program code that were executed in the first environment. The code coverage profiling system determines a second code coverage profile associated with execution of the program code in a second environment. The second code coverage profile indicates one or more portions of the program code that were executed in the second environment. The code coverage profiling system performs a comparison of the first code coverage profile and the second code coverage profile. The comparison determines a difference between the portions of the program code that were executed in the first and second environments.

Analysis of code coverage differences across environments

Methods, systems, and computer-readable media for distributed canary testing with test artifact caching are disclosed. Using one or more storage components, a test client stores one or more software artifacts for testing of a software product. The client initiates a first test of the software product using the software artifact(s) stored in the storage component(s). In the first test, the client sends a first set of requests to the software product at a first point in time. The client initiates a second test of the software product using the software artifact(s) stored in the storage component(s). In the second test, the client sends a second set of requests to the software product at a second point in time. The software artifact(s) are maintained in the storage component(s) between the first point in time and the second point in time.

Distributed canary testing with test artifact caching

Methods, systems, and computer-readable media for creation of software tests matching production personas are disclosed. A software testing system determines a plurality of observations regarding execution of a software product in a production environment comprising a plurality of software products. The software testing system determines one or more personas based at least in part on analysis of the plurality of observations. A particular persona represents one or more usage characteristics shared by a subset of clients of the software product. The software testing system generates one or more tests associated with the particular persona. The one or more tests comprise one or more input values of a plurality of calls. The one or more input values represent the usage characteristics shared by the subset of clients of the software product.

Creation of software tests matching production personas

Techniques are described for aggregating code coverage data generated from various types of testing of software modules, and automatically determining whether to promote software upwards in a multi-level software deployment hierarchy based on the aggregated code coverage data. In embodiments, a code coverage metric is determined for a software module, and the metric is compared to a set of promotion criteria, including whether the metric meets a predetermined threshold for quality. In some cases, the threshold may be a general threshold, a threshold based on the level of possible promotion, and/or a threshold that is based on an identified category for the software module such as whether the module is a front-end module, a shared module, a legacy module, or a critical module.

Promotion determination based on aggregated code coverage metrics

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Seattle, WA, United States of America

Mihaela Petrescu