Amazon Technologies, Inc.

Vijay Pratap Singh

Carlos Alejandro Arguelles

Michael Luis Collado

Mihaela Petrescu

Siamak Irantash

Choi Yong Ngo

Slavik Dimitrovich

Justin Alexander Tisi

Venkata Uday Kumar Kalepalli Naga

Kiran Chitluri

Ramakrishnan Hariharan Chandrasekharapuram

Michael C Moore

Mike C Moore

Mikhail Khasanov

Choi Young Ngo

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

In some implementations, a testing service receives a test execution request for executing test operations on a test target. The testing service may map the test execution request to a particular type of supported test framework from among a plurality of types of supported test frameworks. The testing service may obtain a test package provided by a user that requested the testing, such as from a cloud storage location. The testing service determines a computing capacity for executing the testing and appropriates a plurality of workers in a cloud computing service. The testing service configures the plurality of workers for executing the test operations based on at least one of the test framework, the test execution request or the test package. The testing service provides test execution chunks from the test package to the plurality of workers for executing the testing on the test target.

Cloud-based test execution

Systems and associated processes for testing a reverse proxy server are disclosed. A backend proxy server test system can receive a request from a reverse proxy server under test. The request may be generated in response to a request from a test client to access a backend service. In responding to the received request, the backend proxy server test system can include a copy of the received request. Upon the test client receiving the response from the proxy server to the test client's request, the test client can extract the embedded copy of the received request that the reverse proxy server generated to determine whether it matches the request that a functioning reverse proxy server generates. Based, at least in part on the result of this comparison, the test client can determine whether the reverse proxy server is malfunctioning.

Proxy server testing

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Seattle, WA, United States of America

Vijay Pratap Singh