60 lines
2.3 KiB
R
60 lines
2.3 KiB
R
library(tidyverse)
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# load in the human labels and for each task filer, @ the task level
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# GET the proportion of Observed bug behavior + Expected Behavior
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# GET the proportion of Solution Discussion + Solution Usage
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human_csv <-"~/dsl/092225_info_matt_labels.csv"
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human_df <- read.csv(human_csv, header = TRUE)
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#task_authors <- human_df %>%
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# filter(comment_type == "task_description") %>%
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# select(TaskPHID, AuthorPHID) %>%
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# rename(Task_AuthorPHID = AuthorPHID)
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#result <- task_authors %>%
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# rowwise() %>%
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# mutate(
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# bug_prop = {
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# rows_by_author <- human_df %>% filter(AuthorPHID == task_authorPHID)
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# mean(rows_by_author$label %in% c("Observed bug behavior", "Expected behavior"))
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# }
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# ) %>%
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# ungroup()
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human_result <- human_df %>%
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group_by(TaskPHID) %>%
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summarise(
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human_BE_prop = mean(human_label %in% c("OBSERVED BUG BEHAVIOR", "EXPECTED BEHAVIOR")),
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human_SOL_prop = mean(human_label %in% c("SOLUTION DISCUSSION", "SOLUTION USAGE"))
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)
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# load in the existing df and @ the task level get
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# for each task filer,
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# GET the proportion of Observed bug behavior + Expected Behavior
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# GET the proportion of Solution Discussion + Solution Usage
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main_csv <-"~/analysis_data/092925_unified_phab.csv"
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main_df <- read.csv(main_csv, header = TRUE)
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closed_relevance_summary <- main_df %>%
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filter(comment_type == "task_description") %>%
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select(TaskPHID, closed_relevance, priority_score, source, phase, week_index)
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# TODO: need to get the TaskAuthor's comments, not just the big picture
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machine_result <- main_df %>%
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mutate(olmo_label = str_extract_all(olmo_sentence_categories, "(?<=')[^']+(?=')")) |>
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unnest(olmo_label) |>
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group_by(TaskPHID) |>
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summarise(
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olmo_BE_prop = mean(olmo_label %in% c("OBSERVED BUG BEHAVIOR", "EXPECTED BEHAVIOR")),
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olmo_SOL_prop = mean(olmo_label %in% c("SOLUTION DISCUSSION", "SOLUTION USAGE")),
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median_gerrit_delta = median(gerrit_code_insertions + gerrit_code_deletions, na.rm = TRUE),
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median_gerrit_reviewers = median(gerrit_reviewer_count, na.rm = TRUE),
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n_comments = sum(comment_type == "task_subcomment")
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) |>
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left_join(closed_relevance_summary, by = "TaskPHID") |>
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mutate(dsl_score = ifelse(closed_relevance == "True", 1, 0))
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output_df <- machine_result |>
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left_join(human_result, by = "TaskPHID")
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write.csv(output_df, "093025_power_dsl.csv", row.names = FALSE)
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