# RACK: Automatic API Recommendation using Crowdsourced Knowledge

arXiv: https://arxiv.org/abs/1807.02953 · HTML: https://mlsanity.com/paper/1807.02953/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 12 of 12)

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- [A Systematic Review of Automated Query Reformulations in Source Code Search](https://mlsanity.com/paper/2108.09646/) (2021)
- [ARIST: An Effective API Argument Recommendation Approach](https://mlsanity.com/paper/2306.06620/) (2023)
- [Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network](https://mlsanity.com/paper/2104.01032/) (2021)
- [FACOS: Finding API Relevant Contents on Stack Overflow with Semantic and Syntactic Analysis](https://mlsanity.com/paper/2111.07238/) (2021)
- [Software Entity Recognition with Noise-Robust Learning](https://mlsanity.com/paper/2308.10564/) (2023)
- [Improved Bug Localization with AI Agents Leveraging Hypothesis and Dynamic Cognition](https://mlsanity.com/paper/2601.12522/) (2026)
- [A Hybrid LTR-based System via Social Context Embedding for Recommending Solutions of Software Bugs in Developer Communities](https://mlsanity.com/paper/2603.07229/) (2026)
- [From Pen to Pixel: Translating Hand-Drawn Plots into Graphical APIs via a Novel Benchmark and Efficient Adapter](https://mlsanity.com/paper/2603.26356/) (2026)

## Abstract (arXiv)

Traditional code search engines often do not perform well with natural language queries since they mostly apply keyword matching. These engines thus need carefully designed queries containing information about programming APIs for code search. Unfortunately, existing studies suggest that preparing an effective code search query is both challenging and time consuming for the developers. In this paper, we propose a novel API recommendation technique--RACK that recommends a list of relevant APIs for a natural language query for code search by exploiting keyword-API associations from the crowdsourced knowledge of Stack Overflow. We first motivate our technique using an exploratory study with 11 core Java packages and 344K Java posts from Stack Overflow. Experiments using 150 code search queries randomly chosen from three Java tutorial sites show that our technique recommends correct API classes within the top 10 results for about 79% of the queries which is highly promising. Comparison with two variants of the state-of-the-art technique also shows that RACK outperforms both of them not only in Top-K accuracy but also in mean average precision and mean recall by a large margin.