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The decline of Stack Overflow · Which fields were affected the most by the rise of AI? ilo.im/1635mr

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Tomaž Weiss · The Decline of Stack OverflowFor many years, Stack Overflow has been the go-to platform for developers seeking solutions to coding problems. However, with the rise of AI-driven coding assistants, the platform’s relevance has taken a hit. In this post, we’ll explore the data behind Stack Overflow’s decline, comparing trends in question volume and shifts in popular tags after the introduction of ChatGPT and other LLMs.Other articles have explored this trend as well, such as thisanalysis by DevClassand this post by Pragmatic Engineer. However, these discussions primarily focus on overall activity decline rather than specific changes in question tags. In this post, we will take a closer look at which topics have been most affected by the shift to AI-driven assistance.Additionally, it is interesting to examine which topics have been the least affected by AI, highlighting areas where human assistance remains valuable.The data for this analysis was obtained from the StackExchange Data Explorerwhich allows SQL queries against their databases.A Sharp Decline in Question VolumeThe time series chart below illustrates a significant drop in question volume, particularly after November 2022, when ChatGPT was publicly released. Prior to this, Stack Overflow had already been experiencing a slow decline, but after ChatGPT’s introduction, the rate of decline became much more pronounced as developers increasingly turned to AI for quick, context-aware answers.Which Topics Are Affected the Most?To better understand the impact, we analyzed how the number of questions for the most popular tags in November 2022 changed by November 2024. While some topics have declined more than others, which is to be expected, there is an interestingpattern in these declines.What stands out is that questions related to fundamental programming concepts (such as lists, dictionaries, loops, strings, and functions) and data analysis (including pandas, dataframes, arrays, SQL, and NumPy) have experienced the most significant declines.It is possible that AI-generated answers for the most impacted topics are of particularly high quality, making human assistance less necessary. Alternatively, developers working with these technologies may be more inclined to incorporate AI into their workflow, reducing their reliance on traditional Q&A platforms.Conversely, topics related to operating systems (Windows, Android, and iOS) and certain development frameworks, IDEs and cloud platforms (including Next.js, .NET, SwiftUI, React Native, Angular, Android Studio, Visual Studio Code, and Azure) have experienced comparatively smaller decreases in activity.This could be due to the fact that questions in these areas frequently involve frontend and UI-related challenges or require visual explanations with screenshots, elements where AI-generated responses still struggle to provide adequate support.A more comprehensive interpretation of these results is left to readers with a deeper understanding of the listed technologies.Found this content interesting or useful? Fuel future articles with a coffee!The CodeTime seriesQuery:-- number of questions over timeselect DATETRUNC(day, p.CreationDate) as day, count(*) as n_questionsfrom Posts pwhere PostTypeId = 1 -- questiongroup by DATETRUNC(day, p.CreationDate)order by 1;Plot:# renv::init()library(tidyverse)library(janitor)library(ggplot2)library(slider)# data import -------------------------------------------------------------data <- read_csv('data/counts_by_day.csv') |> clean_names()# data preparation --------------------------------------------------------data <- data |> mutate(day = as.Date(day)) |> arrange(day) |> mutate(rolling_avg = slider::slide_period_dbl( .x = n_questions, .i = day, .period = "day", .f = mean, .before = 27, .complete = FALSE ))# plot --------------------------------------------------------------------# arrow locationarrow_x <- as.Date("2022-11-30")arrow_y <- data$rolling_avg[data$day == arrow_x] # corresponding y valuefig <- data |> ggplot(aes(x = day, y = rolling_avg)) + geom_line(size = 1, color = 'steelblue') + scale_x_date(date_breaks = "1 year", date_labels = "%Y", minor_breaks = NULL, expand = expansion(add = c(30, 30))) + scale_y_continuous(breaks = seq(0, 7000, by = 1000), expand = expansion(add = c(5, 80))) + labs(title = "Number of Stack Overflow Questions over Time", subtitle = 'Showing 28 day rolling average.', x = "Date", y = "Number of Questions") + annotate("text", x = max(data$day), y = 6600, label = "tomazweiss.github.io \nData Source: StackExchange Data Explorer", hjust = 1, vjust = 0, size = 4) + theme_light() + annotate("segment", x = arrow_x + 100, y = arrow_y + 1000, xend = arrow_x + 0, yend = arrow_y + 150, arrow = arrow(type = "closed", length = unit(0.15, "inches")), color = "black") + annotate("text", x = arrow_x + 350, y = arrow_y + 1100, label = "ChatGPT launches", hjust = 1, size = 4, color = "black")figggsave('plots/so_timeseries.png', plot = fig, width = 400, height = 200, units = 'mm', dpi = 'retina')Percentage Decrease by TagQuery:-- monthly number of questions by tag for most popular tagswith target_tags as (select TagName as tag_name, count(*) as cnt from Posts p inner join PostTags pt on p.Id = pt.PostId inner join Tags t on pt.TagId = t.Id where PostTypeId = 1 -- question and DATETRUNC(mm, p.CreationDate) between '2022-01-01' and '2022-12-01' group by TagName having count(*) >= 7000 )select tt.tag_name, DATETRUNC(mm, p.CreationDate) as month, count(*) as n_questionsfrom Posts p inner join PostTags pt on p.Id = pt.PostId inner join Tags t on pt.TagId = t.Id inner join target_tags tt on t.TagName = tt.tag_namewhere PostTypeId = 1 -- question and DATETRUNC(mm, p.CreationDate) between '2022-11-01' and '2025-02-01'group by tt.tag_name, DATETRUNC(mm, p.CreationDate)order by 1, 2;Plot:library(tidyverse)library(janitor)# data import -------------------------------------------------------------data <- read_csv('data/QueryResults_month.csv') |> clean_names()# data preparation --------------------------------------------------------data <- data |> mutate(month = ymd(month))starting_values <- data |> filter(month == as.Date("2022-11-01")) ending_values <- data |> filter(month == as.Date("2024-11-01")) data_plot <- starting_values |> left_join(ending_values, by = 'tag_name') |> mutate(n_questions_end = replace_na(n_questions_end, 0)) |> mutate(ratio = n_questions_end / n_questions_start) |> mutate(decline_pct = -100*(1 - ratio)) |> arrange(decline_pct)data_plot <- data_plot |> mutate(tag_name = paste0(tag_name, ' (', n_questions_start, ')')) data_plot$tag_name <- factor(data_plot$tag_name, levels = data_plot$tag_name)# plot --------------------------------------------------------------------fig <- data_plot |> ggplot(aes(x = decline_pct, y = tag_name)) + geom_col(fill = "steelblue") + theme_light() + scale_x_continuous( labels = function(x) paste0(x, "%"), # Add % symbol to labels breaks = seq(-100, 0, by = 10), expand = expansion(add = c(0.5, 0.5)), sec.axis = sec_axis(~ ., breaks = seq(-100, 0, by = 10), labels = function(x) paste0(x, "%") ) ) + labs( title = "Percentage Drop in Number of Stack Overflow Questions by their Tag", subtitle = paste0("Comparing November 2022 to November 2024.\n", "Showing 75 most popular tags of 2022. Numbers in brackets next to tag names represent number of questions in November 2022."), x = "Percentage Decrease", y = "Tag Name" ) + annotate("label", x = 0, y = 0, label = "tomazweiss.github.io \nData Source: StackExchange Data Explorer", hjust = 1.03, vjust = -0.3, size = 3.5, fill = "white", label.size = 0)figggsave('plots/so_decline.png', plot = fig, width = 300, height = 300, units = 'mm', dpi = 'retina')GitHubhttps://github.com/tomazweiss/stackoverflow_decline
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