Software products are built from a number of interacting binaries that are rolled out asynchronously, at differing schedules and paces. When there is a problem with a software product such as a failure or sub-par performance, identifying the binaries that are the source of a product failure or sub-par performance is difficult with end-to-end tests. Additionally, an apparently failing test can be a false positive that does require any code changes to fix, yet that demands human attention. This disclosure describes techniques to automatically determine, from end-to-end tests of a software product, code binaries that are at the root of product failure or sub-par performance. For each failure signal, a history is maintained of post-triage manual feedback. The history is used to generate a confidence metric, referred to as a cube score, of a new failure being a true positive. Probable false positive test failures can be filtered out, reducing developer effort to address the apparent test failure.
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Sharma, Shubham; Puri, Varun; and Elumalai, Babu Prasad, "Identification of Faulty Software Binaries from End-to-End Tests", Technical Disclosure Commons, (December 12, 2022)