zoekt/internal/e2e/testdata/ at b227501acf82ca21a07a6cf1d7b36616a2b21327 · boltless.me/zoekt · Tangled

boltless.me / zoekt

0

fork of https://github.com/sourcegraph/zoekt

0

zoekt / internal / e2e / testdata /

at b227501acf82ca21a07a6cf1d7b36616a2b21327 15 files

Keegan Carruthers-Smith score: introduce query.Boost to scale score (#728) 2y ago

Get_databaseuser.txt

score: introduce query.Boost to scale score (#728) This commit introduces a new primitive Boost to our query language. It allows boosting (or dampening) the contribution to the score a query atoms will match contribute. To achieve this we introduce boostMatchTree which records this weight. We then adjust the visitMatches to take an initial score weight (1.0), and then each time we recurse through a boostMatchTree the score weight is multiplied by the boost weight. Additionally candidateMatch now has a new field, scoreWeight, which records the weight at time of candidate collection. Without boosting in the query this value will always be 1. Finally when scoring a candidateMatch we take the final score for it and multiply it by scoreWeight. Note: we do not expose a way to set this in the query language, only the query API. Test Plan: Manual testing against webserver via the new phrase-boost URL param. Additionally updated ranking tests to use the phrase booster.

2 years ago

InternalDoer.txt

e2e: recall and visual indication of ranks (#714) This adds a new field to the golden files "targetRank" which records the rank of the document we are looking for. Additionally the document is marked with "**" in the golden files. Additionally we add a new golden file which contains recall@1, recall@5 and the MRR. I set the target documents by looking at the existing results and guessing which was the one we wanted based on memory. In some cases we no longer had the top document, for example for generate unit test. Test Plan: go test

2 years ago

Repository_metadata_Write_rbac.txt

e2e: recall and visual indication of ranks (#714) This adds a new field to the golden files "targetRank" which records the rank of the document we are looking for. Additionally the document is marked with "**" in the golden files. Additionally we add a new golden file which contains recall@1, recall@5 and the MRR. I set the target documents by looking at the existing results and guessing which was the one we wanted based on memory. In some cases we no longer had the top document, for example for generate unit test. Test Plan: go test

2 years ago

assets_are_not_configured_for_this_binary.txt

score: introduce query.Boost to scale score (#728) This commit introduces a new primitive Boost to our query language. It allows boosting (or dampening) the contribution to the score a query atoms will match contribute. To achieve this we introduce boostMatchTree which records this weight. We then adjust the visitMatches to take an initial score weight (1.0), and then each time we recurse through a boostMatchTree the score weight is multiplied by the boost weight. Additionally candidateMatch now has a new field, scoreWeight, which records the weight at time of candidate collection. Without boosting in the query this value will always be 1. Finally when scoring a candidateMatch we take the final score for it and multiply it by scoreWeight. Note: we do not expose a way to set this in the query language, only the query API. Test Plan: Manual testing against webserver via the new phrase-boost URL param. Additionally updated ranking tests to use the phrase booster.

2 years ago

bufio_buffer.txt

e2e: add a ranking test case for "zoekt searcher" (#720) This example was given in our channel recently as a good result, so lets keep track of it to ensure we don't regress. Test Plan: go test

2 years ago

bufio_flush_writer.txt

e2e: add a ranking test case for "zoekt searcher" (#720) This example was given in our channel recently as a good result, so lets keep track of it to ensure we don't regress. Test Plan: go test

2 years ago

bytes_buffer.txt

e2e: recall and visual indication of ranks (#714) This adds a new field to the golden files "targetRank" which records the rank of the document we are looking for. Additionally the document is marked with "**" in the golden files. Additionally we add a new golden file which contains recall@1, recall@5 and the MRR. I set the target documents by looking at the existing results and guessing which was the one we wanted based on memory. In some cases we no longer had the top document, for example for generate unit test. Test Plan: go test

2 years ago

coverage_data_writer.txt

e2e: add ranking tests for atom boosting (#715) This adds four cases which are exact phrases to search for. "assets are not configured for this binary" finds the correct document, but it isn't shown in the summary. Our target rank doesn't capture that this could be better, but I will use the golden file to see if I improve this. "sourcegraph/server docker image build" is an example of an exact phrase which also happens to contain words of highly ranked symbols. This leads to the exact phrase getting buried. I want to see if I can boost that. "bufio flush writer" should find the symbol bufioFlushWriter. "coverage data writer" should find the symbol CoverageDataWriter. Test Plan: go test

2 years ago

generate_unit_test.txt

score: introduce query.Boost to scale score (#728) This commit introduces a new primitive Boost to our query language. It allows boosting (or dampening) the contribution to the score a query atoms will match contribute. To achieve this we introduce boostMatchTree which records this weight. We then adjust the visitMatches to take an initial score weight (1.0), and then each time we recurse through a boostMatchTree the score weight is multiplied by the boost weight. Additionally candidateMatch now has a new field, scoreWeight, which records the weight at time of candidate collection. Without boosting in the query this value will always be 1. Finally when scoring a candidateMatch we take the final score for it and multiply it by scoreWeight. Note: we do not expose a way to set this in the query language, only the query API. Test Plan: Manual testing against webserver via the new phrase-boost URL param. Additionally updated ranking tests to use the phrase booster.

2 years ago

graphql_type_User.txt

e2e: recall and visual indication of ranks (#714) This adds a new field to the golden files "targetRank" which records the rank of the document we are looking for. Additionally the document is marked with "**" in the golden files. Additionally we add a new golden file which contains recall@1, recall@5 and the MRR. I set the target documents by looking at the existing results and guessing which was the one we wanted based on memory. In some cases we no longer had the top document, for example for generate unit test. Test Plan: go test

2 years ago

r_cody_sourcegraph_url.txt

e2e: recall and visual indication of ranks (#714) This adds a new field to the golden files "targetRank" which records the rank of the document we are looking for. Additionally the document is marked with "**" in the golden files. Additionally we add a new golden file which contains recall@1, recall@5 and the MRR. I set the target documents by looking at the existing results and guessing which was the one we wanted based on memory. In some cases we no longer had the top document, for example for generate unit test. Test Plan: go test

2 years ago

score: introduce query.Boost to scale score (#728) This commit introduces a new primitive Boost to our query language. It allows boosting (or dampening) the contribution to the score a query atoms will match contribute. To achieve this we introduce boostMatchTree which records this weight. We then adjust the visitMatches to take an initial score weight (1.0), and then each time we recurse through a boostMatchTree the score weight is multiplied by the boost weight. Additionally candidateMatch now has a new field, scoreWeight, which records the weight at time of candidate collection. Without boosting in the query this value will always be 1. Finally when scoring a candidateMatch we take the final score for it and multiply it by scoreWeight. Note: we do not expose a way to set this in the query language, only the query API. Test Plan: Manual testing against webserver via the new phrase-boost URL param. Additionally updated ranking tests to use the phrase booster.

2 years ago

sourcegraphserver_docker_image_build.txt

score: introduce query.Boost to scale score (#728) This commit introduces a new primitive Boost to our query language. It allows boosting (or dampening) the contribution to the score a query atoms will match contribute. To achieve this we introduce boostMatchTree which records this weight. We then adjust the visitMatches to take an initial score weight (1.0), and then each time we recurse through a boostMatchTree the score weight is multiplied by the boost weight. Additionally candidateMatch now has a new field, scoreWeight, which records the weight at time of candidate collection. Without boosting in the query this value will always be 1. Finally when scoring a candidateMatch we take the final score for it and multiply it by scoreWeight. Note: we do not expose a way to set this in the query language, only the query API. Test Plan: Manual testing against webserver via the new phrase-boost URL param. Additionally updated ranking tests to use the phrase booster.

2 years ago

test_server.txt

e2e: add a ranking test case for "zoekt searcher" (#720) This example was given in our channel recently as a good result, so lets keep track of it to ensure we don't regress. Test Plan: go test

2 years ago

zoekt_searcher.txt

e2e: add a ranking test case for "zoekt searcher" (#720) This example was given in our channel recently as a good result, so lets keep track of it to ensure we don't regress. Test Plan: go test

2 years ago